5 papers · 1 filter
The Role of Measured Covariates in Assessing Sensitivity to Unmeasured Confounding
Abhinandan Dalal, Iris Horng, Yang Feng +1
Sensitivity analysis is widely used to assess the robustness of causal conclusions in observational studies, yet its interaction with the structure of measured covariates is often…
Planning for gold: Hypothesis screening with split samples for valid powerful testing in matched observational studies
William Bekerman, Abhinandan Dalal, Carlo del Ninno +1
Observational studies are valuable tools for inferring causal effects in the absence of controlled experiments. However, these studies may be biased due to the presence of some rel…
Partial Identification of Causal Effects for Endogenous Continuous Treatments
Abhinandan Dalal, Eric J. Tchetgen Tchetgen
No unmeasured confounding is a common assumption when reasoning about counterfactual outcomes, but such an assumption may not be plausible in observational studies. Sensitivity ana…
Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
Abhinandan Dalal, Patrick Blöbaum, Shiva Kasiviswanathan +1
Double (debiased) machine learning (DML) has seen widespread use in recent years for learning causal/structural parameters, in part due to its flexibility and adaptability to high-…
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
Abhinav Chakraborty, Anirban Chatterjee, Abhinandan Dalal
The Ising model, originally developed as a spin-glass model for ferromagnetic elements, has gained popularity as a network-based model for capturing dependencies in agents' outputs…