research
∙
09/01/2023
Causal Effect Estimation after Propensity Score Trimming with Continuous Treatments
Most works in causal inference focus on binary treatments where one esti...
share
research
∙
06/30/2023
Minimax optimal subgroup identification
Quantifying treatment effect heterogeneity is a crucial task in many are...
research
∙
05/23/2023
Incremental Propensity Score Effects for Criminology: An Application Assessing the Relationship Between Houselessness, Behavioral Health Problems, and Recidivism
This study examines the relationship between houselessness and recidivis...
research
∙
05/06/2023
The Fundamental Limits of Structure-Agnostic Functional Estimation
Many recent developments in causal inference, and functional estimation ...
research
∙
04/26/2023
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
The average treatment effect, which is the difference in expectation of ...
research
∙
01/31/2023
Efficient Generalization and Transportation
When estimating causal effects, it is important to assess external valid...
research
∙
01/28/2023
Covariate-assisted bounds on causal effects with instrumental variables
When an exposure of interest is confounded by unmeasured factors, an ins...
research
∙
01/15/2023
Doubly Robust Counterfactual Classification
We study counterfactual classification as a new tool for decision-making...
research
∙
12/07/2022
Nonparametric Estimation of Conditional Incremental Effects
Conditional effect estimation has great scientific and policy importance...
research
∙
10/15/2022
Heterogeneous interventional indirect effects with multiple mediators: non-parametric and semi-parametric approaches
We propose semi- and non-parametric methods to estimate conditional inte...
research
∙
07/24/2022
Fast convergence rates for dose-response estimation
We consider the problem of estimating a dose-response curve, both global...
research
∙
07/19/2022
The role of the geometric mean in case-control studies
Historically used in settings where the outcome is rare or data collecti...
research
∙
03/12/2022
Semiparametric doubly robust targeted double machine learning: a review
In this review we cover the basics of efficient nonparametric parameter ...
research
∙
03/02/2022
Minimax rates for heterogeneous causal effect estimation
Estimation of heterogeneous causal effects - i.e., how effects of polici...
research
∙
11/13/2021
drpop: Efficient and Doubly Robust Population Size Estimation in R
This paper introduces the R package drpop to flexibly estimate total pop...
research
∙
10/20/2021
Incremental causal effects: an introduction and review
In this chapter, we review the class of causal effects based on incremen...
research
∙
04/29/2021
Doubly robust capture-recapture methods for estimating population size
Estimation of population size using incomplete lists (also called the ca...
research
∙
04/16/2021
Semiparametric Sensitivity Analysis: Unmeasured Confounding In Observational Studies
Establishing cause-effect relationships from observational data often re...
research
∙
03/29/2021
Comment on "Statistical Modeling: The Two Cultures" by Leo Breiman
Motivated by Breiman's rousing 2001 paper on the "two cultures" in stati...
research
∙
03/11/2021
Doubly robust confidence sequences for sequential causal inference
This paper derives time-uniform confidence sequences (CS) for causal eff...
research
∙
03/02/2021
Median Optimal Treatment Regimes
Optimal treatment regimes are personalized policies for making a treatme...
research
∙
02/24/2021
Semiparametric counterfactual density estimation
Causal effects are often characterized with averages, which can give an ...
research
∙
11/25/2020
Doubly Robust Adaptive LASSO for Effect Modifier Discovery
Effect modification occurs when the effect of the treatment on an outcom...
research
∙
09/07/2020
Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds
Algorithmic fairness is a topic of increasing concern both within resear...
research
∙
06/30/2020
Counterfactual Predictions under Runtime Confounding
Algorithms are commonly used to predict outcomes under a particular deci...
research
∙
06/17/2020
Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning"
We congratulate the authors on their exciting paper, which introduces a ...
research
∙
04/29/2020
Optimal doubly robust estimation of heterogeneous causal effects
Heterogeneous effect estimation plays a crucial role in causal inference...
research
∙
12/05/2019
Sensitivity Analysis via the Proportion of Unmeasured Confounding
In observational studies, identification of ATEs is generally achieved b...
research
∙
10/08/2019
Causal Inference for Comprehensive Cohort Studies
In a comprehensive cohort study of two competing treatments (say, A and ...
research
∙
08/30/2019
Counterfactual Risk Assessments, Evaluation, and Fairness
Algorithmic risk assessments are increasingly used to help humans make d...
research
∙
07/09/2019
Incremental Intervention Effects in Studies with Many Timepoints, Repeated Outcomes, and Dropout
Modern longitudinal studies feature data collected at many timepoints, o...
research
∙
11/03/2018
Instrumental Variable Methods using Dynamic Interventions
Recent work on dynamic interventions has greatly expanded the range of c...
research
∙
10/08/2018
Visually Communicating and Teaching Intuition for Influence Functions
Estimators based on influence functions (IFs) have been shown effective ...
research
∙
10/01/2018
A nonparametric projection-based estimator for the probability of causation, with application to water sanitation in Kenya
Current estimation methods for the probability of causation (PC) make st...
research
∙
06/08/2018
Causal effects based on distributional distances
We develop a novel framework for estimating causal effects based on the ...
research
∙
02/25/2018
Efficient nonparametric causal inference with missing exposure information
In this note we study identifiability and efficient estimation of causal...
research
∙
01/11/2018
Sharp instruments for classifying compliers and generalizing causal effects
It is well-known that, without restricting treatment effect heterogeneit...
research
∙
11/20/2017