Causal Inference through a Witness Protection Program

By Ricardo M. A. Silva, Robin J. Evans. 2014

21 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Counterfactual Fairness (2017, background): “Other alternatives include exploiting bounds on the contribution of hidden variables [29, 33].”
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation (2017, background): “In recent years, researchers have been exploring the relationships between causal inference and machine learning [39, 10, 38].”