Examples of the disclosure are directed toward generating a causation score with respect to an agent and an outcome, and projecting a future causation score distribution. For example, a causation score may be determined with respect to a hypothesis that a given agent causes a given outcome, and the score may indicate the acceptance of that hypothesis in the scientific community, as described by scientific literature. A future causation score distribution, then, may indicate a probability distribution over possible future causation scores, thereby predicting the scientific acceptance of the hypothesis at some specific date in the future. A future causation score distribution can be projected by first generating one or more future publication datasets, and then determining causation scores for each of the one or more future publication datasets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of computing a future causation score, the method comprising: determining a plurality of distributions based on a publication dataset, wherein each distribution is a subset of the publication dataset; generating a future publication dataset based on a weighted mixture of the plurality of distributions; and computing a future causation score based on the future publication dataset. 2. The method of claim 1, further comprising: generating a plurality of additional future publication datasets based on the weighted mixture of the plurality of distributions; and computing a causation score distribution based on the plurality of additional future publication datasets. 3. The method of claim 1, wherein the publication dataset consists of publication metadata. 4. The method of claim 1, further comprising: determining an additional plurality of distributions based on the future publication dataset, wherein each of the additional plurality of distributions is a subset of the future publication dataset; and generating an additional future publication dataset based on a weighted mixture of the additional plurality of distributions. 5. The method of claim 1, wherein generating a future publication dataset includes sampling the weighted mixture of the plurality of distributions a number of times, wherein the number of times is a sampling count determined based on a random walk from a publication rate of the publication dataset. 6. A non-transitory computer readable storage medium storing instructions executable to perform a method of computing a future causation score, the method comprising: determining a plurality of distributions based on a publication dataset, wherein each distribution is a subset of the publication dataset; generating a future publication dataset based on a weighted mixture of the plurality of distributions; and computing a future causation score based on the future publication dataset. 7. The non-transitory computer readable storage medium of claim 6, the method further comprising: generating a plurality of additional future publication datasets based on the weighted mixture of the plurality of distributions; and computing a causation score distribution based on the plurality of additional future publication datasets. 8. The non-transitory computer readable storage medium of claim 6, wherein the publication dataset consists of publication metadata. 9. The non-transitory computer readable storage medium of claim 6, the method further comprising: determining an additional plurality of distributions based on the future publication dataset, wherein each of the additional plurality of distributions is a subset of the future publication dataset; and generating an additional future publication dataset based on a weighted mixture of the additional plurality of distributions. 10. The non-transitory computer readable storage medium of claim 6, wherein generating a future publication dataset includes sampling the weighted mixture of the plurality of distributions a number of times, wherein the number of times is a sampling count determined based on a random walk from a publication rate of the publication dataset.