Bond girl Effect on career
What had an effect on the career?
What had an effect on a Bond girl's career?
The role of a Bond girl, as it has evolved in the films, is typically a high-profile part that can sometimes give a major boost to the career of unestablished actresses, although a number of Bond girls were well-established beforehand. For instance, Diana Rigg and Honor Blackman were both cast as Bond girls after they had already become stars in the United Kingdom for their roles in the television series, The Avengers. In addition, Halle Berry won an Academy Award in 2002--the award was presented to her while she was filming Die Another Day. Teri Hatcher was already known for her role as Lois Lane in the television series Lois & Clark: The New Adventures of Superman before she was cast in Tomorrow Never Dies. A few years after playing a Bond girl, she became one of the most highly paid actresses on television, starring in Desperate Housewives. Jane Seymour was an unknown when she was cast in Live and Let Die (the opening credits read "Introducing Jane Seymour"), later won an Emmy Award for playing Maria Callas in a TV movie and then became a household name playing the title role in her TV series Dr. Quinn, Medicine Woman. Kim Basinger had perhaps the most successful post-Bond career. After her break-out role in Never Say Never Again, Basinger went on to win an Academy Award for her performance in L.A. Confidential and to star in a number of blockbuster films, including Batman and 8 Mile, over the next two decades.  Broccoli's original choice for the role of Domino Derval was Julie Christie following her performance in Billy Liar in 1963. Upon meeting her personally, however, he was disappointed that she did not have the full figure he was looking for and turned his attentions towards Raquel Welch after seeing her on the cover of the October 1964 issue of Life magazine. Welch, however, was hired by Richard Zanuck of 20th Century Fox to appear in the film Fantastic Voyage the same year instead. French actress Claudine Auger was ultimately cast in the role. Thunderball launched Auger into a successful European film career but did little for her in the United States.  At one time it was said that appearing as a Bond girl would damage an actress's career. Lois Chiles is often cited as a case in point, even though her career did not suffer because of her portrayal of Holly Goodhead, but rather because, after she lost her younger brother to non-Hodgkin lymphoma, she decided to take a three-year break from acting, from which her career never recovered. Casting for the female lead in Casino Royale (2006) was hindered by potential actresses' concerns about the effect that playing the role might have on their careers. At that point, some thought that the Bond series had become stale and would therefore be a less desirable vehicle for young actresses. Nevertheless, the up-and-coming actress Eva Green agreed to play the role of Vesper Lynd, and showed those fears to be unfounded when she won BAFTA's Rising Star Award for her performance. Rosamund Pike, who made her feature film debut as Miranda Frost in Die Another Day (2002), went on to earn an Academy Award Nomination for Gone Girl. CANNOTANSWER
is typically a high-profile part that can sometimes give a major boost to the career of unestablished actresses,
A Bond girl is a character who is a love interest or female companion of James Bond in a novel, film or video game. Bond girls occasionally have names that are double entendres or puns, such as Pussy Galore, Plenty O'Toole, Xenia Onatopp, or Holly Goodhead.

A Bond girl can also refer to an actress playing a Bond girl, such as Ursula Andress, Honor Blackman or Sophie Marceau.

There is no set rule on what kind of person a Bond girl will be or what role she will play. She may be an ally or an enemy of Bond, pivotal to the mission or simply there for her looks. There are female characters such as Judi Dench's M, and Camille Montes, a Bolivian intelligence agent who teams up with Bond in Quantum of Solace, who are not romantic interests of Bond, and hence not strictly Bond girls. However, it has been argued that M's pivotal role in the plot of Skyfall qualifies her as a Bond girl or Bond woman.

The term Bond girl may also be considered as an anachronism, particularly in the post #MeToo era, with regard to the objectification of women in film.

In novels

Nearly all of Ian Fleming's Bond novels and short stories include one or more female characters who can be said to qualify as Bond girls, most of whom have been adapted for the screen. While Fleming's Bond girls have some individual traits (at least in their literary forms), they also have a great many characteristics in common. One of these is age: The typical Bond girl is in her early to mid-twenties, roughly ten years younger than Bond, who seems to be perennially in his mid-thirties. Examples include Solitaire (25), Tatiana Romanova (24), Vivienne "Viv" Michel (23), and Kissy Suzuki (23). The youngest Bond girl (though she and Bond do not sleep together) may be Gala Brand; she is named for the cruiser in which her father is serving at the time of her birth. Bond's youngest sexual partner in the books is Mariko Ichiban, an 18-year-old masseuse in You Only Live Twice. The eldest Bond girls are Pussy Galore, who Bond speculates is in her early 30s, and 29-year-old Domino Vitali.

Bond girls conform to a fairly well-defined standard of beauty. They possess splendid figures and tend to dress in a slightly masculine, assertive fashion, wear little jewellery—and that in a masculine cut—wide leather belts, and square-toed leather shoes. (There is some variation in dress, though: Bond girls have made their initial appearances in evening wear, in bra and panties and, on occasion, naked.) Most are white; they often sport light though noticeable suntans (although a few, such as Solitaire, Tatiana Romanova, and Pussy Galore, are not only tanless but remarkably pale), and they generally use little or no makeup and no nail polish, also wearing their nails short. Their hair may be any colour, though they typically wear it in a natural or casual cut that falls heavily to their shoulders. Their features, especially their eyes and mouths, are often widely spaced (e.g. Vesper Lynd, Gala Brand, Tiffany Case, Tatiana Romanova, Honey Ryder, Viv Michel, Mary Goodnight). Their eyes are usually blue (e.g. Vesper Lynd, Gala Brand, Tatiana Romanova, Honey Ryder, Tracy Bond, Mary Goodnight), and sometimes this is true to an unusual and striking degree: Tiffany Case's eyes are chatoyant, varying with the light from grey to grey-blue, while Pussy Galore has deep violet eyes, the only truly violet eyes that Bond had ever seen. The first description of a Bond girl, Casino Royale's Vesper Lynd, is almost a template for the typical dress as well as the general appearance of later Bond girls; she sports nearly all of the features discussed above. In contrast, Dominetta "Domino" Vitali arguably departs to the greatest degree from the template, dressing in white leather doeskin sandals, appearing more tanned, sporting a soft Brigitte Bardot haircut, and giving no indication of widely spaced features. (The departure may be due to the unusual circumstances behind the writing of the novel Thunderball, in which Domino appears.) Even Domino, however, wears rather masculine jewellery.

The best-known characteristic of Bond girls apart from their uniform beauty is their pattern of sexually suggestive names, such as Pussy Galore. Names with less obvious meanings are sometimes explained in the novels. While Solitaire's real name is Simone Latrelle, she is known as Solitaire because she excludes men from her life; Gala Brand, as noted above, is named for her father's cruiser, HMS Galatea; and Tiffany Case received her name from her father, who was so angry that she was not a boy that he gave her mother a thousand dollars and a compact from Tiffany's and then walked out on her. Fleming's penchant for double-entendre names began with the first Bond novel Casino Royale. Conjecture is widespread that the name of the Bond girl in that novel, "Vesper Lynd," was intended to be a pun on "West Berlin," signifying Vesper's divided loyalties as a double agent under Soviet control. Several Bond girls, however, have normal names (e.g. Tatiana Romanova, Mary Ann Russell, Judy Havelock, Viv Michel, Tracy Bond (née Teresa Draco, aka Contessa Teresa di Vicenzo).

Most Bond girls are apparently (and sometimes expressly) sexually experienced by the time they meet Bond. Quite often those previous experiences have not been positive, and many Bond girls have had sexual violence inflicted on them in the past which has caused them to feel alienated from all men—until Bond comes along. Tiffany Case was gang-raped as a teenager; Honey Ryder, too, was beaten and raped as a teenager by a drunken acquaintance. Pussy Galore was sexually abused at age 12 by her uncle. While there is no such clear-cut trauma in Solitaire's early life, there are suggestions that she, too, avoids men because of their unwanted sexual advances in her past. Kissy Suzuki reports to Bond that during her brief career in Hollywood, when she was 17, "They thought that because I am Japanese I am some sort of an animal and that my body is for everyone." The implication is often that these violent episodes have turned the Bond girls in question against men, though upon encountering Bond they overcome their earlier antipathy and sleep with him not only willingly but eagerly. The cliché reaches its most extreme level in Goldfinger. In this novel Pussy Galore is portrayed as a lesbian when she first meets Bond, but at the end of the novel she sleeps with him. When, in bed, he says to her, "They told me you only liked women," she replies, "I never met a man before."

In Fleming's novels, many Bond girls have some sort of independent job or even career, often one that was considered inappropriate for women in the 1950s. Vesper Lynd, Gala Brand, Tatiana Romanova, Mary Ann Russell, and Mary Goodnight are in intelligence or law-enforcement work. Those who are criminals, such as Tiffany Case and Pussy Galore, tend to be similarly independent-minded in how they approach their work—the latter even running her own syndicate. Even those Bond girls who have more conventional or glamorous jobs show themselves to be invested in having an independent outlook on life. While the Bond girls are clearly intended as sex objects, they are nevertheless portrayed in the novels as having a high degree of independence; this is also frequently (but not always) the case in the films.

Most of the novels focus on one particular romance, as some of them do not begin until well into the novel (Casino Royale is a good example). However, several exceptions have been made: In Goldfinger, the Masterton sisters are considered Bond girls (although Tilly is supposedly a lesbian), and after their deaths, Pussy Galore (also supposedly a lesbian) becomes the primary Bond girl. In Thunderball, Bond romances first Patricia Fearing, then later Domino Vitali. In On Her Majesty's Secret Service, Bond enters into a relationship and an eventual marriage with Teresa 'Tracy' di Vicenzo, and sleeps with Ruby Windsor, a patient he meets in Blofeld's hideout while posing as a genealogist. In You Only Live Twice, Bond mainly has a relationship with Kissy Suzuki, but also romances Mariko Ichiban, as well as another girl.

Several Bond girls have obvious signs of inner turmoil (Vesper Lynd or Vivienne Michel), and others have traumatic pasts. Most Bond girls whose characters are allowed to develop in the course of the story are flawed, and several have unhappy sexual backgrounds (Honey Ryder, Pussy Galore, Tiffany Case, Vivienne Michel, and Kissy Suzuki, among others).

Inspiration
The inspiration for all of Fleming's Bond girls may be his onetime lover Muriel Wright, who according to The Times

Wright was 26 and "exceptionally beautiful" when she and Fleming met in 1935. A talented rider, skier, and polo player, Wright was independently wealthy and a model. She was devoted to Fleming, despite his repeated unfaithfulness. She died in an air raid in 1944, devastating Fleming, who called Wright "too good to be true".

In film

Ursula Andress (as Honey Ryder) in Dr. No (1962) is widely regarded as the first Bond girl, although she was preceded by both Eunice Gayson as Sylvia Trench and Zena Marshall as Miss Taro in the same film. Goldfinger (1964), the third, established the Bond girl as regularly appearing in Bond films.

There have been many attempts to break down the numerous Bond girls into a top 10 list for the entire series; characters who often appear in these lists include Anya Amasova (from The Spy Who Loved Me, portrayed by Barbara Bach); Pussy Galore (from Goldfinger, portrayed by Honor Blackman); Contessa Teresa di Vicenzo (from On Her Majesty's Secret Service, portrayed by Diana Rigg); and often ranked Number 1 on the list, Honey Ryder (from Dr. No, portrayed by Ursula Andress).

Entertainment Weekly put "Bond bathing suits" on its end-of-the-decade, "best-of" list, saying, "And you thought spies were supposed to be inconspicuous! Halle Berry's orange bikini in Die Another Day (2002) and Daniel Craig's supersnug powder blue trunks in Casino Royale (2006) suggest that neither 007 star can keep a secret."

Monica Bellucci in Spectre became the oldest Bond girl at the age of 50, although she stated that she does not consider herself to be a "Bond girl", but a "Bond woman".

Roles and impact
Roald Dahl said that when writing You Only Live Twice, he was advised to use three Bond girls: The first should die "preferably in Bond's arms" early, the second a villain whom Bond seduces before she dies in an unusual and gory way midway, and the third survives to the end of the film. In several, the Bond girl is revealed, after her tryst with Bond, to be a villainess. Examples are Fatima Blush (Barbara Carrera) in Never Say Never Again (1983), Elektra King (Sophie Marceau) in The World Is Not Enough (1999), and Miranda Frost (Rosamund Pike) in Die Another Day (2002).  The Dalton films of the 1980s introduced the "Bond woman", who is equal to and challenges Bond, but he remains the heterosexual hero; they are depicted with Dalton and later Bonds and their cars and gadgets, implying that all are possessions that Bond can use and dispose.

As of 2013, there have been only two films in which James Bond falls in love with the Bond girl. The first was On Her Majesty's Secret Service (1969), in which Countess Tracy di Vicenzo (Diana Rigg) marries Bond but is shot dead by Irma Bunt and Ernst Stavro Blofeld at the story's end. The second was Vesper Lynd (Eva Green) in Casino Royale (2006). Bond confesses his love to her and resigns from MI6 so that they can have a normal life together. He later learns that she had been a double agent working for his enemies. The enemy organisation Quantum had kidnapped her former lover and had been blackmailing her to secure her co-operation. She ends up actually falling in love with Bond, but dies, as Quantum is closing in on her, by drowning in a lift in a building under renovation in Venice.

With the exception of these two doomed Bond girls, it is never explained why Bond's love interest in one film is gone by the next, and is never mentioned or even alluded to again. This is not always the case in the novels, which do sometimes make references to the Bond girls who have appeared in previous books. Tiffany Case and Honey Ryder are revealed to have married other men (in From Russia With Love and The Man With the Golden Gun respectively), and in Doctor No, Bond briefly wonders about Solitaire. In John Gardner's novels continuing the franchise Bond girls begin to appear in more than one book, often picking up their relationships with Bond from before, and in one case continuing a romance through two consecutive titles. In Licence Renewed it is specifically noted in an epilogue that Bond and Lavender Peacock stopped seeing each other after a brief romance, but Sukie Tempesta (Nobody Lives for Ever), Beatrice Maria da Ricci (Win, Lose or Die), and Fredericka von Grüsse (Never Send Flowers) all make return appearances in later books.  Anthony Horowitz's Trigger Mortis picks up two weeks after the events in Goldfinger with Bond continuing his relationship with Pussy Galore. A unique case is Mary Goodnight, who appears in the novels, On Her Majesty's Secret Service and You Only Live Twice as Bond's secretary, before becoming a full-fledged Bond girl in The Man With the Golden Gun.

Effect on career
The role of a Bond girl, as it has evolved in the films, is typically a high-profile part that can sometimes give a major boost to the career of unestablished actresses, although a number of Bond girls were well-established beforehand. For instance, Diana Rigg and Honor Blackman were both cast as Bond girls after they had already become stars in the United Kingdom for their roles in the television series The Avengers. In addition, Halle Berry won an Academy Award in 2002—the award was presented to her while she was filming Die Another Day. Teri Hatcher was already known for her role as Lois Lane in the television series Lois & Clark: The New Adventures of Superman before she was cast in Tomorrow Never Dies. A few years after playing a Bond girl, she became one of the most highly paid actresses on television, starring in Desperate Housewives.  Jane Seymour was an unknown when she was cast in Live and Let Die (the opening credits read "Introducing Jane Seymour"), later won an Emmy Award for playing Maria Callas in a TV movie and then became a household name playing the title role in her TV series Dr. Quinn, Medicine Woman. Kim Basinger had perhaps the most successful post-Bond career. After her break-out role in Never Say Never Again, Basinger went on to win an Academy Award for her performance in L.A. Confidential and to star in a number of blockbuster films, including Batman and 8 Mile, over the next two decades.

Broccoli's original choice for the role of Domino Derval was Julie Christie following her performance in Billy Liar in 1963. It seems he was disappointed when he met her so instead he considered Raquel Welch after seeing her on the cover of the October 1964 issue of Life magazine. Welch, however, was hired by Richard Zanuck of 20th Century Fox to appear in the film Fantastic Voyage the same year instead. French actress Claudine Auger was ultimately cast in the role. Thunderball launched Auger into a successful European film career but did little for her in the United States.

At one time, it was said that appearing as a Bond girl would damage an actress's career. Lois Chiles is often cited as a case in point, even though her career did not suffer because of her portrayal of Holly Goodhead, but rather because, after she lost her younger brother to non-Hodgkin lymphoma, she decided to take a three-year break from acting, from which her career never recovered. Casting for the female lead in Casino Royale (2006) was hindered by potential actresses' concerns about the effect that playing the role might have on their careers. At that point, some thought that the Bond series had become stale and would therefore be a less desirable vehicle for young actresses. Nevertheless, the up-and-coming actress Eva Green agreed to play the role of Vesper Lynd, and showed those fears to be unfounded when she won BAFTA's Rising Star Award for her performance. Rosamund Pike, who made her feature film debut as Miranda Frost in Die Another Day (2002), went on to earn an Academy Award Nomination for Gone Girl.

Multiple appearances
Prior to the series being rebooted in 2006 with Casino Royale, Sylvia Trench was the only Bond girl character to appear in more than one film (Dr. No in 1962 and From Russia with Love in 1963). She was meant to be Bond's regular girlfriend, but was dropped after her appearance in the second film. After the series was rebooted, Moneypenny was re-introduced in Skyfall (2012) as an agent assisting Bond in his mission and her characterisation was closer to that of a Bond girl; following her demotion at the end of Skyfall the character returned for the next film, Spectre (2015), as M's personal assistant and the characterisation of Moneypenny was closer to that of the original series. Léa Seydoux, who played Madeleine Swann in Spectre, reprised her role in No Time to Die (2021).

In the Eon series, six actresses have made reappearances as different Bond girls: Martine Beswick and Nadja Regin both first appeared in From Russia with Love, and then appeared in Thunderball and Goldfinger respectively. Maud Adams played Andrea Anders in The Man with the Golden Gun (1974) and the eponymous character in Octopussy (1983).

If the non-Eon produced films, Casino Royale and Never Say Never Again, are included, several actresses have also been a Bond girl more than once: Ursula Andress in Dr. No (1962) and Casino Royale; Angela Scoular in On Her Majesty's Secret Service (1969) and Casino Royale; Valerie Leon in The Spy Who Loved Me (1977) and Never Say Never Again (1983).

List of Bond girls

Ian Fleming stories

Mary Goodnight was a supporting character in several Bond novels before graduating to full Bond girl in The Man with the Golden Gun. Several short stories, such as "Quantum of Solace", "The Hildebrand Rarity", "The Living Daylights", and "The Property of a Lady" feature female characters in prominent roles, but none of these women interact with Bond in a romantic way.

Post-Fleming stories

Eon Productions films
There are several different archetypes for Bond girls: romantic interests, those who assist him, femme fatales (who invariably make an attempt on Bond's life), and sacrificial lambs (female associates of Bond who wind up dead). Since it is debatable whether certain girls fulfil certain tropes (e.g. If Bond kisses a girl, does that make her a romantic interest? Is Pussy Galore a "femme fatale" due to her being in league with Goldfinger?) the following criteria are used for determining inclusion: women with whom sexual encounters are implied; the woman who principally assists Bond; femme fatales are taken to be women who attempt to kill Bond; sacrificial lambs are taken to be women with an allegiance to Bond whose death is instigated by the main villain or his henchmen.

Non-Eon films
In addition to the Eon Productions films, there have been two Bond films produced by independent studios and one television production.  The roles are not as easily categorized.

Video games

Documentary
In 2002 former Bond girl Maryam d'Abo co-wrote the book Bond Girls Are Forever: The Women of James Bond. This book later became a DVD exclusive documentary featuring d'Abo and other Bond girls, including Ursula Andress. In some locations, the documentary was released as a gift with the purchase of Die Another Day on DVD. The featurette was included on the DVD release of Casino Royale (2006).

See also

 Outline of James Bond
 :Category:Bond girls

References

Bibliography
 
 
 

Lists of actors by role
Lists of fictional females
Lists of fictional sidekicks
Girl
 

de:Figuren aus James-Bond-Filmen#Die Bond-Girls
The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name "Rubin causal model" was first coined by Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis, though he discussed it only in the context of completely randomized experiments.  Rubin extended it into a general framework for thinking about causation in both observational and experimental studies.

Introduction

The Rubin causal model is based on the idea of potential outcomes. For example, a person would have a particular income at age 40 if they had attended college, whereas they would have a different income at age 40 if they had not attended college.  To measure the causal effect of going to college for this person, we need to compare the outcome for the same individual in both alternative futures. Since it is impossible to see both potential outcomes at once, one of the potential outcomes is always missing. This dilemma is the "fundamental problem of causal inference". 

Because of the fundamental problem of causal inference, unit-level causal effects cannot be directly observed. However, randomized experiments allow for the estimation of population-level causal effects. A randomized experiment assigns people randomly to treatments: college or no college. Because of this random assignment, the groups are (on average) equivalent, and the difference in income at age 40 can be attributed to the college assignment since that was the only difference between the groups. An estimate of the average causal effect (also referred to as the average treatment effect) can then be obtained by computing the difference in means between the treated (college-attending) and control (not-college-attending) samples. 

In many circumstances, however, randomized experiments are not possible due to ethical or practical concerns. In such scenarios there is a non-random assignment mechanism. This is the case for the example of college attendance: people are not randomly assigned to attend college. Rather, people may choose to attend college based on their financial situation, parents' education, and so on. Many statistical methods have been developed for causal inference, such as propensity score matching.  These methods attempt to correct for the assignment mechanism by finding control units similar to treatment units.

An extended example

Rubin defines a causal effect:

Intuitively, the causal effect of one treatment, E, over another, C, for a particular unit and an interval of time from  to  is the difference between what would have happened at time  if the unit had been exposed to E initiated at  and what would have happened at  if the unit had been exposed to C initiated at : 'If an hour ago I had taken two aspirins instead of just a glass of water, my headache would now be gone,' or 'because an hour ago I took two aspirins instead of just a glass of water, my headache is now gone.'  Our definition of the causal effect of the E versus C treatment will reflect this intuitive meaning."

According to the RCM, the causal effect of your taking or not taking aspirin one hour ago is the difference between how your head would have felt in case 1 (taking the aspirin) and case 2 (not taking the aspirin).  If your headache would remain without aspirin but disappear if you took aspirin, then the causal effect of taking aspirin is headache relief. In most circumstances, we are interested in comparing two futures, one generally termed "treatment" and the other "control". These labels are somewhat arbitrary.

Potential outcomes

Suppose that Joe is participating in an FDA test for a new hypertension drug.  If we were omniscient, we would know the outcomes for Joe under both treatment (the new drug) and control (either no treatment or the current standard treatment). The causal effect, or treatment effect, is the difference between these two potential outcomes.

 is Joe's blood pressure if he takes the new pill.  In general, this notation expresses the potential outcome which results from a treatment, t, on a unit, u.  Similarly,  is the effect of a different treatment, c or control, on a unit, u.  In this case,  is Joe's blood pressure if he doesn't take the pill.   is the causal effect of taking the new drug.

From this table we only know the causal effect on Joe.  Everyone else in the study might have an increase in blood pressure if they take the pill. However, regardless of what the causal effect is for the other subjects, the causal effect for Joe is lower blood pressure, relative to what his blood pressure would have been if he had not taken the pill.

Consider a larger sample of patients:

The causal effect is different for every subject, but the drug works for Joe, Mary and Bob because the causal effect is negative. Their blood pressure is lower with the drug than it would have been if each did not take the drug. For Sally, on the other hand, the drug causes an increase in blood pressure.

In order for a potential outcome to make sense, it must be possible, at least a priori. For example, if there is no way for Joe, under any circumstance, to obtain the new drug, then  is impossible for him. It can never happen. And if  can never be observed, even in theory, then the causal effect of treatment on Joe's blood pressure is not defined.

No causation without manipulation

The causal effect of new drug is well defined because it is the simple difference of two potential outcomes, both of which might happen. In this case, we (or something else) can manipulate the world, at least conceptually, so that it is possible that one thing or a different thing might happen.

This definition of causal effects becomes much more problematic if there is no way for one of the potential outcomes to happen, ever. For example, what is the causal effect of Joe's height on his weight? Naively, this seems similar to our other examples. We just need to compare two potential outcomes: what would Joe's weight be under the treatment (where treatment is defined as being 3 inches taller) and what would Joe's weight be under the control (where control is defined as his current height).

A moment's reflection highlights the problem: we can't increase Joe's height. There is no way to observe, even conceptually, what Joe's weight would be if he were taller because there is no way to make him taller. We can't manipulate Joe's height, so it makes no sense to investigate the causal effect of height on weight. Hence the slogan: No causation without manipulation.

Stable unit treatment value assumption (SUTVA)

We require that "the [potential outcome] observation on one unit should be unaffected by the particular assignment of treatments to the other units" (Cox 1958, §2.4).  This is called the stable unit treatment value assumption (SUTVA), which goes beyond the concept of independence.

In the context of our example, Joe's blood pressure should not depend on whether or not Mary receives the drug.  But what if it does? Suppose that Joe and Mary live in the same house and Mary always cooks.  The drug causes Mary to crave salty foods, so if she takes the drug she will cook with more salt than she would have otherwise.  A high salt diet increases Joe's blood pressure.  Therefore, his outcome will depend on both which treatment he received and which treatment Mary receives.

SUTVA violation makes causal inference more difficult.  We can account for dependent observations by considering more treatments.  We create 4 treatments by taking into account whether or not Mary receives treatment.

Recall that a causal effect is defined as the difference between two potential outcomes. In this case, there are multiple causal effects because there are more than two potential outcomes.  One is the causal effect of the drug on Joe when Mary receives treatment and is calculated, .  Another is the causal effect on Joe when Mary does not receive treatment and is calculated .  The third is the causal effect of Mary's treatment on Joe when Joe is not treated. This is calculated as .  The treatment Mary receives has a greater causal effect on Joe than the treatment which Joe received has on Joe, and it is in the opposite direction.

By considering more potential outcomes in this way, we can cause SUTVA to hold.  However, if any units other than Joe are dependent on Mary, then we must consider further potential outcomes.  The greater the number of dependent units, the more potential outcomes we must consider and the more complex the calculations become (consider an experiment with 20 different people, each of whose treatment status can effect outcomes for every one else).  In order to (easily) estimate the causal effect of a single treatment relative to a control, SUTVA should hold.

Average causal effect

Consider:

One may calculate the average causal effect by taking the mean of all the causal effects. 

How we measure the response affects what inferences we draw.  Suppose that we measure changes in blood pressure as a percentage change rather than in absolute values. Then, depending in the exact numbers, the average causal effect might be an increase in blood pressure.  For example, assume that George's blood pressure would be 154 under control and 140 with treatment. The absolute size of the causal effect is −14, but the percentage difference (in terms of the treatment level of 140) is −10%. If Sarah's blood pressure is 200 under treatment and 184 under control, then the causal effect in 16 in absolute terms but 8% in terms of the treatment value.  A smaller absolute change in blood pressure (−14 versus 16) yields a larger percentage change (−10% versus 8%) for George. Even though the average causal effect for George and Sarah is +1 in absolute terms, it is −1 in percentage terms.

The fundamental problem of causal inference

The results we have seen up to this point would never be measured in practice.  It is impossible, by definition, to observe the effect of more than one treatment on a subject over a specific time period.  Joe cannot both take the pill and not take the pill at the same time.  Therefore, the data would look something like this:

Question marks are responses that could not be observed. The Fundamental Problem of Causal Inference is that directly observing causal effects is impossible.  However, this does not make causal inference impossible.  Certain techniques and assumptions allow the fundamental problem to be overcome.

Assume that we have the following data:

We can infer what Joe's potential outcome under control would have been if we make an assumption of constant effect:  

 

and

 

If we wanted to infer the unobserved values we could assume a constant effect. The following tables illustrates data consistent with the assumption of a constant effect.

All of the subjects have the same causal effect even though they have different outcomes under the treatment.

The assignment mechanism

The assignment mechanism, the method by which units are assigned treatment, affects the calculation of the average causal effect.  One such assignment mechanism is randomization.  For each subject we could flip a coin to determine if she receives treatment.  If we wanted five subjects to receive treatment, we could assign treatment to the first five names we pick out of a hat.  When we randomly assign treatments we may get different answers.

Assume that this data is the truth:

The true average causal effect is −8. But the causal effect for these individuals is never equal to this average. The causal effect varies, as it generally (always?) does in real life. After assigning treatments randomly, we might estimate the causal effect as:

A different random assignment of treatments yields a different estimate of the average causal effect.

The average causal effect varies because our sample is small and the responses have a large variance.  If the sample were larger and the variance were less, the average causal effect would be closer to the true average causal effect regardless of the specific units randomly assigned to treatment.

Alternatively, suppose the mechanism assigns the treatment to all men and only to them.

Under this assignment mechanism, it is impossible for women to receive treatment and therefore impossible to determine the average causal effect on female subjects.  In order to make any inferences of causal effect on a subject, the probability that the subject receive treatment must be greater than 0 and less than 1.

The perfect doctor

Consider the use of the perfect doctor as an assignment mechanism.  The perfect doctor knows how each subject will respond to the drug or the control and assigns each subject to the treatment that will most benefit her.  The perfect doctor knows this information about a sample of patients:

Based on this knowledge she would make the following treatment assignments:

The perfect doctor distorts both averages by filtering out poor responses to both the treatment and control. The difference between means, which is the supposed average causal effect, is distorted in a direction that depends on the details. For instance, a subject like Susie who is harmed by taking the drug would be assigned to the control group by the perfect doctor and thus the negative effect of the drug would be masked.

Conclusion

The causal effect of a treatment on a single unit at a point in time is the difference between the outcome variable with the treatment and without the treatment.  The Fundamental Problem of Causal Inference is that it is impossible to observe the causal effect on a single unit.  You either take the aspirin now or you don't. As a consequence, assumptions must be made in order to estimate the missing counterfactuals.

The Rubin causal model has also been connected to instrumental variables (Angrist, Imbens, and Rubin, 1996) and other techniques for causal inference. For more on the connections between the Rubin causal model, structural equation modeling, and other statistical methods for causal inference, see Morgan and Winship (2007).

See also
Causation
Principal stratification
Propensity score matching

References

Further reading 
 Guido Imbens & Donald Rubin (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press. doi:10.1017/CBO9781139025751
 Donald Rubin  (1977) "Assignment to Treatment Group on the Basis of a Covariate", Journal of Educational Statistics, 2, pp. 1–26.
 Rubin, Donald (1978) "Bayesian Inference for Causal Effects: The Role of Randomization", The Annals of Statistics, 6, pp. 34–58.

External links
 "Rubin Causal Model": an article for the New Palgrave Dictionary of Economics by Guido Imbens and Donald Rubin.
 "Counterfactual Causal Analysis":  a webpage maintained by Stephen Morgan, Christopher Winship, and others with links to many research articles on causal inference.

Causal inference
Statistical models
Econometric models
Observational study
Experiments In experimental psychology, the tunnel effect is the perception as a single object moving beyond an occluding object and then reappearing after a suitable amount of time on the other side of it. This phenomenon has been studied by Burke (1952), who discovered that the optimal amount of time for giving the impression of a single object is shorter than what is actually needed to cross the occlusion at that speed.
The "tunnel effect", when talking about long stretches of road, refers to the environment surrounding the driver that begins to merge towards the central point of the horizon. This effect can be noted at high speeds, when driving on straight smooth roads. The effect is amplified if the environment surrounding is monotonous. The "tunnelling effect" can cause nausea, confusion to drivers as well as letting fatigue settle in at a higher rate, making it one major cause in sleep related accidents.

References
 G. Bruno Vicario and Y. Kiritani, Slow-Motion Tunnel Effect: an Enquiry into Vertical Organization of Perceptual Events
 L. Burke (1952). "On the tunnel effect". The Quarterly Journal of Experimental Psychology. Vol. 4. Issue 3. pp. 121–138.
Ranney TA, Simmons LA, Masalonis AJ. (1999). Prolonged exposure to glare and driving time: effects on performance in a driving simulator. p 601–610 Accident Analysis Prevention;
Experimental psychology