Methods and apparatus are provided for causal learning in which logical causes of events are determined based, at least in part, on causal saliency. One example method for causal learning generally includes observing one or more events with an apparatus, wherein the events are defined as occurrences at particular relative times; selecting a subset of the events based on one or more criteria; and determining a logical cause of at least one of the events based on the selected subset.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for causal learning, comprising: observing one or more events with an apparatus, wherein the events are defined as occurrences at particular relative times; selecting a subset of the events based on one or more criteria; and determining a logical cause of at least one of the events based on the selected subset. 2. The method of claim 1, wherein the criteria comprise causal saliency, defined as a degree to which one event stands out from other events. 3. The method of claim 2, wherein the more often an unpredictable event takes place, the more causally salient the unpredictable event is. 4. The method of claim 1, wherein the criteria comprise at least one of recurrence, distinctiveness, or temporal proximity. 5. The method of claim 1, wherein the selecting comprises considering the earliest of the events providing statistically significant information about another one of the events as the most important events. 6. The method of claim 5, further comprising storing the most important events in a memory. 7. The method of claim 1, wherein the observing comprises: periodically sampling a system to generate a set of discrete points; and converting the set of discrete points to the events. 8. The method of claim 1, wherein the method is implemented in an artificial nervous system capable of inference learning. 9. The method of claim 1, further comprising repeating the selecting and the determining if a new event is observed. 10. The method of claim 1, further comprising predicting one or more subsequent events based on the logical cause. 11. An apparatus for causal learning, comprising: a processing system configured to: observe one or more events, defined as occurrences at particular relative times; select a subset of the events based on one or more criteria; and determine a logical cause of at least one of the events based on the selected subset; and a memory coupled to the processing system. 12. The apparatus of claim 11, wherein the criteria comprise causal saliency, defined as a degree to which one event stands out from other events. 13. The apparatus of claim 12, wherein the more often an unpredictable event takes place, the more causally salient the unpredictable event is. 14. The apparatus of claim 11, wherein the criteria comprise at least one of recurrence, distinctiveness, or temporal proximity. 15. The apparatus of claim 11, wherein the processing system is configured to select the subset of the events by considering the earliest of the events providing statistically significant information about another one of the events as the most important events. 16. The apparatus of claim 15, wherein the most important events are stored in the memory. 17. The apparatus of claim 11, wherein the processing system is configured to observe the one or more events by: periodically sampling a system to generate a set of discrete points; and converting the set of discrete points to the events. 18. The apparatus of claim 11, wherein the apparatus is part of an artificial nervous system capable of inference learning. 19. The apparatus of claim 11, wherein the processing system is further configured to repeat the selecting and the determining if a new event is observed. 20. The apparatus of claim 11, wherein the processing system is further configured to predict one or more subsequent events based on the logical cause. 21. An apparatus for causal learning, comprising: means for observing one or more events, defined as occurrences at particular relative times; means for selecting a subset of the events based on one or more criteria; and means for determining a logical cause of at least one of the events based on the selected subset. 22. A computer program product for causal learning, comprising a non-transitory computer-readable medium having code for: observing one or more events, defined as occurrences at particular relative times; selecting a subset of the events based on one or more criteria; and determining a logical cause of at least one of the events based on the selected subset.