Search Results for author:
Found 5 papers, 0 papers with code
DIET: Conditional independence testing with marginal dependence measures of residual information
DIET tests the marginal independence of two random variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a conditional cumulative distribution function (CDF).
General Control Functions for Causal Effect Estimation from IVs
Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders.
Model-Agnostic Feature Selection with Additional Mutual Information
We show that f-divergences provide a broad class of proper test statistics.
General Control Functions for Causal Effect Estimation from Instrumental Variables
Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders.
Removing Hidden Confounding by Experimental Grounding
We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data.
