10 papers
Heterogeneous Effects of Continuous Treatments via Conditional Modified Treatment Policies
Samhita Pal, Jared D Huling
For continuous treatments such as drug dose or ventilator intensity, a key clinically actionable question is whether a modest, patient-specific adjustment to the current dose would…
B-CALM: Bias-Limited Bayesian Borrowing for RCT-Anchored Treatment Effects under Covariate Mismatch
Amir Asiaee, Samhita Pal
Randomized controlled trials (RCTs) identify treatment effects in the randomized trial population but are often too small for reliable heterogeneity estimation; observational studi…
Sharp Bounds for Treatment Effect Generalization under Outcome Distribution Shift
Amir Asiaee, Samhita Pal, Cole Beck +1
Generalizing treatment effects from a randomized trial to a target population requires the assumption that potential outcome distributions are invariant across populations after co…
Omitted-Variable Sensitivity Analysis for Generalizing Randomized Trials
Amir Asiaee, Samhita Pal, Jared D. Huling
Randomized controlled trials (RCTs) yield internally valid causal effect estimates, but generalizing these results to target populations with different characteristics requires an…
Improving RCT-Based CATE Estimation Under Covariate Mismatch via Double Calibration
Samhita Pal, Jared D. Huling, Amir Asiaee
We develop estimators that improve precision of heterogeneous treatment effect estimates that allow borrowing information from observational studies when the available covariates i…
Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Families
Amir Asiaee, Samhita Pal
Many differentially private (DP) data release systems either output DP synthetic data and leave analysts to perform inference as usual, which can lead to severe miscalibration, or…
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
Amir Asiaee, Samhita Pal
Randomized controlled trials (RCTs) are the gold standard for estimating treatment effects, yet they are often underpowered for detecting effect heterogeneity. Large observational…
Causal Discovery with Mixed Latent Confounding via Precision Decomposition
Amir Asiaee, Samhita Pal, James O'quinn +1
We study causal discovery from observational data in linear Gaussian systems affected by \emph{mixed latent confounding}, where some unobserved factors act broadly across many vari…
DAG DECORation: Continuous Optimization for Structure Learning under Hidden Confounding
Samhita Pal, James O'quinn, Kaveh Aryan +3
We study structure learning for linear Gaussian SEMs in the presence of latent confounding. Existing continuous methods excel when errors are independent, while deconfounding-first…
Improving Precision of RCT-Based CATE Estimation using Data Borrowing with Double Calibration
Amir Asiaee, Chiara Di Gravio, Cole Beck +3
Understanding how treatment effects vary across patient characteristics is essential for personalized medicine, yet randomized controlled trials (RCTs) are often underpowered to de…