REVIEW 7 cited by
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
read the original abstract
Estimating heterogeneous treatment effects is an important problem across many domains. In order to accurately estimate such treatment effects, one typically relies on data from observational studies or randomized experiments. Currently, most existing works rely exclusively on observational data, which is often confounded and, hence, yields biased estimates. While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects. In this paper, we propose to estimate heterogeneous treatment effects by combining large amounts of observational data and small amounts of randomized data via representation learning. In particular, we introduce a two-step framework: first, we use observational data to learn a shared structure (in form of a representation); and then, we use randomized data to learn the data-specific structures. We analyze the finite sample properties of our framework and compare them to several natural baselines. As such, we derive conditions for when combining observational and randomized data is beneficial, and for when it is not. Based on this, we introduce a sample-efficient algorithm, called CorNet. We use extensive simulation studies to verify the theoretical properties of CorNet and multiple real-world datasets to demonstrate our method's superiority compared to existing methods.
Forward citations
Cited by 7 Pith papers
-
Optimal Treatment Policy Estimation for Recurrent Events with a Competing Terminal Event: An Instrumented Difference-in-Differences Approach
Develops a multiply robust iDID estimator for optimal policies in recurrent events with terminal competing risk, with simulation results and application to Medicare Type 2 diabetes data.
-
B-CALM: Bias-Limited Bayesian Borrowing for RCT-Anchored Treatment Effects under Covariate Mismatch
Observational contrast information about an RCT treatment-effect surface is capped by the prior precision of a comparative-bias function, so borrowing saturates as observational sample size grows.
-
Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics
dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.
-
Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data
Proposes a minimax-regret framework for learning generalizable CATE models from multisite data by minimizing worst-case regret over convex combinations of site-specific CATEs.
-
B-CALM: Bias-Limited Bayesian Borrowing for RCT-Anchored Treatment Effects under Covariate Mismatch
Observational contrast information about a trial's treatment-effect function is capped by the prior precision of an explicit comparative-bias function, so borrowing saturates and can be tuned as a sensitivity analysis.
-
Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
-
Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.