Name
Abstract | ||
|---|---|---|
One of the most fundamental problems in causal inference is the estimation of a causal effect when treatment and outcome are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest "weak" paths in an unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths. The output is a posterior distribution over bounds on the average causal effect via a linear programming approach and Bayesian inference. We claim this approach should be used in regular practice as a complement to other tools in observational studies. |
Year | Venue | Keywords |
|---|---|---|
2014 | JOURNAL OF MACHINE LEARNING RESEARCH | Causal inference,instrumental variables,Bayesian inference,linear programming |
Field | DocType | Volume |
Econometrics,Causal inference,Frequentist inference,Confounding,Bayesian inference,Instrumental variable,Principal stratification,Posterior probability,Artificial intelligence,Mathematics,Machine learning,Causal model | Conference | 17 |
ISSN | Citations | PageRank |
1532-4435 | 3 | 0.42 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Bezerra de Andrade e Silva | 1 | 109 | 24.56 |
| R. J. Evans | 2 | 18 | 4.48 |