• Publications
  • Influence
Optimal doubly robust estimation of heterogeneous causal effects
A two-stage doubly robust CATE estimator is studied and a generic model-free error bound is given and it is shown that this estimator can be oracle efficient under even weaker conditions, if used with a specialized form of sample splitting and careful choices of tuning parameters.
Non‐parametric methods for doubly robust estimation of continuous treatment effects
A novel kernel smoothing approach is developed that requires only mild smoothness assumptions on the effect curve, and still allows for misspecification of either the treatment density or outcome regression.
Semiparametric theory and empirical processes in causal inference
In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal
Nonparametric Causal Effects Based on Incremental Propensity Score Interventions
  • Edward H. Kennedy
  • Economics
    Journal of the American Statistical Association
  • 1 April 2017
This work characterizes incremental interventions and gives identifying conditions for corresponding effects, and develops general efficiency theory, proposes efficient nonparametric estimators that can attain fast convergence rates even when incorporating flexible machine learning, and explores finite-sample performance via simulation.
Rate of false conviction of criminal defendants who are sentenced to death
Using survival analysis to model this effect, it is estimated that if all death-sentenced defendants remained under sentence of death indefinitely at least 4.1% would be exonerated, which is a conservative estimate of the proportion of false conviction among death sentences in the United States.
Counterfactual risk assessments, evaluation, and fairness
This paper defines counterfactual analogues of common predictive performance and algorithmic fairness metrics that it is argued are better suited for the decision-making context and introduces a new method for estimating the proposed metrics using doubly robust estimation.
Sensitivity Analysis via the Proportion of Unmeasured Confounding
This paper takes a novel approach whereby the sensitivity parameter is the proportion of unmeasured confounding, and derives sharp bounds on the average treatment effect as a function of the sensitivity parameters and proposes nonparametric estimators that allow flexible covariate adjustment.
Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication
The results show differences between the methods in terms of the quantities being estimated and in efficiency, and how the results of a randomized trial of salvage hormone therapy are strongly influenced by the design of the study is demonstrated.
Visually Communicating and Teaching Intuition for Influence Functions
To help foster understanding and trust in IF-based estimators, tangible, visual illustrations of when and how IF- based estimators can outperform standard “plug-in” estimators are presented.
Semiparametric counterfactual density estimation
Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire counterfactual