Regression model
Doubly Robust Estimation (AIPW)
Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.
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Sources
- Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI: 10.1080/01621459.1995.10476494 ↗
- Bang, H. & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973. DOI: 10.1111/j.1541-0420.2005.00377.x ↗
Related methods
Referenced by
Bayesian Doubly Robust EstimationBayesian Entropy BalancingBayesian Inverse Probability WeightingBayesian Marginal Structural ModelBayesian Matching EstimatorBayesian Propensity Score MatchingBayesian Propensity Score WeightingBayesian Sensitivity Analysis for CausalityDouble Machine LearningDoubly Robust Estimation in Education ResearchDynamic Inverse Probability WeightingDynamic Propensity Score MatchingEntropy BalancingG-ComputationHeterogeneous treatment effect Doubly robust estimationHeterogeneous Treatment Effect Entropy BalancingHeterogeneous Treatment Effect Inverse Probability WeightingHeterogeneous Treatment Effect Marginal Structural ModelHeterogeneous Treatment Effect Matching EstimatorHeterogeneous Treatment Effect Propensity Score MatchingHeterogeneous Treatment Effect Sensitivity Analysis for CausalityInverse Probability WeightingInverse Probability Weighting in Education ResearchMachine learning-augmented causal impact analysisMachine Learning-Augmented Coarsened Exact MatchingMachine learning-augmented difference-in-differencesMachine learning-augmented doubly robust estimationMachine Learning-Augmented Entropy BalancingMachine Learning-Augmented Fuzzy Regression DiscontinuityMachine Learning-Augmented Inverse Probability WeightingMachine Learning-Augmented Marginal Structural ModelMachine Learning-Augmented Matching EstimatorMachine Learning-Augmented Propensity Score MatchingMachine learning-augmented propensity score weightingMarginal Structural ModelMatching EstimatorMulti-period Doubly Robust EstimationMulti-period Inverse Probability WeightingMulti-period Propensity Score WeightingPolicy Evaluation Doubly Robust EstimationPolicy Evaluation Inverse Probability WeightingPolicy Evaluation Marginal Structural ModelPolicy Evaluation Propensity Score MatchingPolicy Evaluation Propensity Score WeightingPropensity Score WeightingRobust Counterfactual Impact EvaluationRobust Inverse Probability WeightingRobust Marginal Structural ModelRobust Matching EstimatorRobust Propensity Score MatchingRobust Propensity Score WeightingSensitivity Analysis for CausalitySpatial Doubly Robust EstimationSpatial Inverse Probability WeightingTargeted Maximum Likelihood EstimationTwo-Stage Least Squares (2SLS)