Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
|When:||3rd of November 2017 tot 1st of November 2017|
We cordially invite you to the next Statistics for Structures seminar on Friday next week in Leiden (preceding the Bayes Club).
Ioan Gabriel Bucur (Radboud University Nijmegen): Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Time: 15:00-16:00, November 3, 2017
Location: Niels Bohrweg 1, Leiden, MI, Room 408
Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canonical examples of settings where a closed-form expression exists for the causal effect of one variable on another, given the presence of a third variable. Both rely on faithfulness to infer that the latter only influences the target effect via the cause variable. In reality, it is likely that this assumption only holds approximately and that there will be at least some form of weak interaction. This brings about the paradoxical situation that, in the large-sample limit, no predictions are made, as detecting the weak edge invalidates the setting. We introduce an alternative approach by replacing strict faithfulness with a prior that reflects the existence of many 'weak' (irrelevant) and 'strong' interactions. We obtain a posterior distribution over the target causal effect estimator which shows that, in many cases, we can still make good estimates. We demonstrate the approach in an application on a simple linear-Gaussian setting, using the MultiNest sampling algorithm, and compare it with established techniques to show our method is robust even when strict faithfulness is violated.
This is joint work with Tom Claassen and Tom Heskes
For further information, slides of previous talks and the schedule of planned talks please visit the seminar website: https://mschauer.github.io/StructuresSeminar/