Name
Abstract | ||
|---|---|---|
This paper considers the problem of inferring a discrete joint distribution from a sample subject to selection. Abstractly, we want to identify a distribution p(x, w) from its conditional p(x | w). We introduce new assumptions on the marginal model for p(x), under which generic identification is possible. These assumptions are quite general and can easily be tested; they do not require precise background knowledge of p(x) or p(w), such as proportions estimated from previous studies. We particularly consider conditional independence constraints, which often arise from graphical and causal models, although other constraints can also be used. We show that generic identifiability of causal effects is possible in a much wider class of causal models than had previously been known. |
Year | Venue | Field |
|---|---|---|
2015 | ACI@UAI | Econometrics,Joint probability distribution,Identifiability,Conditional independence,Computer science,Artificial intelligence,Selection bias,Machine learning,Causal model,Marginal model |
DocType | Citations | PageRank |
Conference | 2 | 0.42 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. J. Evans | 1 | 18 | 4.48 |
| Vanessa Didelez | 2 | 16 | 4.03 |