
A Local Method for Identifying Causal Relations under Markov Equivalence
Causality is important for designing interpretable and robust methods in...
read it

Ivy: Instrumental Variable Synthesis for Causal Inference
A popular way to estimate the causal effect of a variable x on y from ob...
read it

Causal aggregation: estimation and inference of causal effects by constraintbased data fusion
Randomized experiments are the gold standard for causal inference. In ex...
read it

Local search for efficient causal effect estimation
Causal effect estimation from observational data is an important but cha...
read it

ConstraintBased Causal Discovery In The Presence Of Cycles
While feedback loops are known to play important roles in many complex s...
read it

Efficiently Learning and Sampling Interventional Distributions from Observations
We study the problem of efficiently estimating the effect of an interven...
read it
Robust Causal Estimation in the LargeSample Limit without Strict Faithfulness
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 closedform 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 largesample 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 linearGaussian setting, using the MultiNest sampling algorithm, and compare it with established techniques to show our method is robust even when strict faithfulness is violated.
READ FULL TEXT
Comments
There are no comments yet.