Inference of Causal Effects when Control Variables are Unknown
Conventional methods in causal effect inferencetypically rely on specifying a valid set of control variables. When this set is unknown or misspecified, inferences will be erroneous. We propose a method for inferring average causal effects when all potential confounders are observed, but thecontrol variables are unknown. When the data-generating process belongs to the class of acyclical linear structural causal models, we prove that themethod yields asymptotically valid confidence intervals. Our results build upon a smooth characterization of linear directed acyclic graphs. We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.
Code (1)
Tasks
validSimilar Papers 제목 키워드 기반
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification impossible. Existing approaches face methodological bottlenecks: clo…
Bayesian InferenceCausal Inference from Text: Unveiling Interactions between Variables
Adjusting for latent covariates is crucial for estimating causal effects from observational textual data. Most existing methods only account for confounding covariates that affect both treatment and outcome, potentially …
Causal InferenceSelection biasBehavioral Causal Inference
When inferring the causal effect of one variable on another from correlational data, a common practice by professional researchers as well as lay decision makers is to control for some set of exogenous confounding variab…
Causal InferenceBlack Box Causal Inference: Effect Estimation via Meta Prediction
Causal inference and the estimation of causal effects plays a central role in decision-making across many areas, including healthcare and economics. Estimating causal effects typically requires an estimator that is tailo…
Causal InferenceDecision MakingPredictionCausal Inference with Categorical Unobserved Confounder via Mixture Learning
Unobserved confounding is a fundamental challenge for estimating causal effects. To address unobserved confounding, recent literature has turned to two different approaches -- proxy variables and the use of multiple trea…
Causal Inference