TY  - GEN
N1  - Copyright © The Authors 2014.
Y1  - 2014/12/13/
AV  - public
TI  - Causal Inference through a Witness Protection Program
KW  - causality
KW  -  Bayesian inference
KW  -  linear programming
A1  - Silva, R
A1  - Evans, R
T3  - Advances in Neural Information Processing Systems
CY  - Montreal, Canada
UR  - http://papers.nips.cc/paper/5602-causal-inference-through-a-witness-protection-program
PB  - Neural Information Processing Systems Foundation
ID  - discovery1471795
N2  - One of the most fundamental problems in causal inference is the estimation of a causal effect when variables 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 a unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of path cancellations'' that will imply conditional independencies but do not rule out the existence of confounding causal paths. The outcome 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 to complement other default tools in observational studies.
ER  -