A Bayesian view of doubly robust causal inference
نویسندگان
چکیده
tion of these. Approaches based on modelling the treatment assignment mechanism, along with their doubly robust extensions, have been difficult to motivate using formal likelihood-based or Bayesian arguments, as the treatment assignment model plays no part in inferences concerning the expected outcomes. On the other hand, forcing dependency between the outcome and treatment assignment models by allowing the former to be misspecified results in loss of the balancing property of the propensity 15
منابع مشابه
Doubly robust estimation and causal inference in longitudinal studies with dropout and truncation by death: Supplementary material
Doubly robust estimation and causal inference in longitudinal studies with dropout and truncation by death: Supplementary material MICHELLE SHARDELL∗,1, GREGORY E HICKS, LUIGI FERRUCCI Department of Epidemiology and Public Health, University of Maryland 660 West Redwood Street Baltimore, Maryland 21201, U.S.A. Department of Physical Therapy, University of Delaware 303 McKinly Lab Newark, Delawa...
متن کاملBounded , efficient and doubly robust estimation with inverse weighting
Consider estimating the mean of an outcome in the presence of missing data or estimating population average treatment effects in causal inference. A doubly robust estimator remains consistent if an outcome regression model or a propensity score model is correctly specified. We build on a previous nonparametric likelihood approach and propose new doubly robust estimators, which have desirable pr...
متن کاملBounded, Efficient, and Doubly Robust Estimation with Inverse Weighting
Consider the problem of estimating the mean of an outcome in the presence of missing data or estimating population average treatment effects in causal inference. A doubly robust estimator remains consistent if an outcome regression model or a propensity score model is correctly specified. We build on the nonparametric likelihood approach of Tan and propose new doubly robust estimators. These es...
متن کاملAn Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
متن کاملKALMANSAC: Causal Inference of Dynamic Processes Drowned in Outliers With Application to Tracking and Real-time Structure From Motion
We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximum-likelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a sampling-based algorithm that has constant complexity. We der...
متن کامل