Search Results for author:
Found 3 papers, 1 papers with code
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
We develop a a new polynomial-time algorithm for identification in linear Structural Causal Models that subsumes previous non-exponential identification methods when applied to direct effects, and unifies several disparate approaches to identification in linear systems.
Orthogonal Statistical Learning with Self-Concordant Loss
Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component.
Long Story Short: Omitted Variable Bias in Causal Machine Learning
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average causal derivatives, and policy effects from covariate shifts.
