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Found 6 papers, 3 papers with code
Hölder Bounds for Sensitivity Analysis in Causal Reasoning
We examine interval estimation of the effect of a treatment T on an outcome Y given the existence of an unobserved confounder U.
Counterfactual Representation Learning with Balancing Weights
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type.
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
We describe a causal transfer random forest (CTRF) that combines existing training data with a small amount of data from a randomized experiment to train a model which is robust to the feature shifts and therefore transfers to a new targeting distribution.
Double Robust Representation Learning for Counterfactual Prediction
Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences.
Enabling Counterfactual Survival Analysis with Balanced Representations
Balanced representation learning methods have been applied successfully to counterfactual inference from observational data.
RNN-based counterfactual prediction, with an application to homestead policy and public schooling
This paper proposes a method for estimating the effect of a policy intervention on an outcome over time.
