← Back to papers
2013 AISTATS AISTATS 2013

Data-driven covariate selection for nonparametric estimation of causal effects

Abstract

The estimation of causal effects from non-experimental data is a fundamental problem in many fields of science. One of the main obstacles concerns confounding by observed or latent covariates, an issue which is typically tackled by adjusting for some set of observed covariates. In this contribution, we analyze the problem of inferring whether a given variable has a causal effect on another and, if it does, inferring an adjustment set of covariates that yields a consistent and unbiased estimator of this effect, based on the (conditional) independence and dependence relationships among the observed variables. We provide two elementary rules that we show to be both sound and complete for this task, and compare the performance of a straightforward application of these rules with standard alternative procedures for selecting adjustment sets.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
📈 Trend Setter — Causal Inference
🧭 Keyword Pioneer — covariate selection
🐣 Hot Topic Early Bird — causal inference
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Doris Entner , Patrik Hoyer , Peter Spirtes

Topics

Artificial Intelligence > Core AI > Causal Inference Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Learning Types > Causal Inference

Keywords

causal inference nonparametric estimation causal effect covariate selection adjustment set
Download PDF

Related papers

Consensus Ranking with Signed Permutations 2013
Ultrahigh Dimensional Feature Screening via RKHS Embeddings 2013
Collapsed Variational Bayesian Inference for Hidden Markov Models 2013
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes 2013
Evidence Estimation for Bayesian Partially Observed MRFs 2013