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07/31/2023
Forster-Warmuth Counterfactual Regression: A Unified Learning Approach
Series or orthogonal basis regression is one of the most popular non-par...
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07/31/2023
Single Proxy Synthetic Control
Synthetic control methods are widely used to estimate the treatment effe...
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06/28/2023
Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift
Statistical machine learning methods often face the challenge of limited...
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04/10/2023
Partial Identification of Causal Effects Using Proxy Variables
Proximal causal inference is a recently proposed framework for evaluatin...
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03/17/2023
Proximal Causal Inference without Uniqueness Assumptions
We consider identification and inference about a counterfactual outcome ...
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02/13/2023
Single Proxy Control
Negative control variables are sometimes used in non-experimental studie...
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02/02/2023
Universal Difference-in-Differences for Causal Inference in Epidemiology
Difference-in-differences is without a doubt the most widely used method...
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12/27/2022
Universal Difference-in-Differences
Difference-in-differences (DiD) is a popular method to evaluate causal e...
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10/24/2022
Rejoinder to discussions on "Instrumental variable estimation of the causal hazard ratio"
We respond to comments on our paper, titled "Instrumental variable estim...
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10/05/2022
Doubly Robust Proximal Synthetic Controls
To infer the treatment effect for a single treated unit using panel data...
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08/02/2022
Doubly Robust Proximal Causal Inference under Confounded Outcome-Dependent Sampling
Unmeasured confounding and selection bias are often of concern in observ...
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07/23/2022
Alternative approaches for analysing repeated measures data that are missing not at random
We consider studies where multiple measures on an outcome variable are c...
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04/21/2022
Structural Nested Mean Models Under Parallel Trends Assumptions
In this paper, we generalize methods in the Difference in Differences (D...
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04/08/2022
Bespoke Instrumental Variables for Causal Inference
Many proposals for the identification of causal effects in the presence ...
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03/23/2022
Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness
The test-negative design (TND) has become a standard approach to evaluat...
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03/11/2022
Distribution-free Prediction Sets Adaptive to Unknown Covariate Shift
Predicting sets of outcomes – instead of unique outcomes – is a promisin...
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03/03/2022
Doubly Robust Calibration of Prediction Sets under Covariate Shift
Conformal prediction has received tremendous attention in recent years a...
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02/09/2022
Evaluating Causal Inference Methods
The fundamental challenge of drawing causal inference is that counterfac...
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01/26/2022
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
We consider the task of identifying and estimating the causal effect of ...
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01/03/2022
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings
In this article, we aim to provide a general and complete understanding ...
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11/17/2021
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets
In the United States and elsewhere, risk assessment algorithms are being...
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11/04/2021
Proximal Causal Inference with Hidden Mediators: Front-Door and Related Mediation Problems
Proximal causal inference was recently proposed as a framework to identi...
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10/20/2021
On Multiply Robust Mendelian Randomization (MR^2) With Many Invalid Genetic Instruments
Mendelian randomization (MR) is a popular instrumental variable (IV) app...
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10/12/2021
Identification and estimation of nonignorable missing outcome mean without identifying the full data distribution
We consider the problem of making inference about the population outcome...
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08/31/2021
Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework
Synthetic control methods are commonly used to estimate the treatment ef...
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07/27/2021
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
We study the problem of observational causal inference with continuous t...
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07/13/2021
GENIUS-MAWII: For Robust Mendelian Randomization with Many Weak Invalid Instruments
Mendelian randomization (MR) has become a popular approach to study caus...
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04/07/2021
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals
A moment function is called doubly robust if it is comprised of two nuis...
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11/17/2020
Semiparametric proximal causal inference
Skepticism about the assumption of no unmeasured confounding, also known...
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10/07/2020
On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable
Unmeasured confounding is a threat to causal inference and individualize...
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09/30/2020
On Mendelian Randomization Mixed-Scale Treatment Effect Robust Identification (MR MiSTERI) and Estimation for Causal Inference
Standard Mendelian randomization analysis can produce biased results if ...
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09/11/2020
A Selective Review of Negative Control Methods in Epidemiology
Purpose of Review: Negative controls are a powerful tool to detect and a...
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04/24/2020
A Simple Weighted Approach for Instrumental Variable Estimation of Marginal Structural Mean Models
Robins 1997 introduced marginal structural models (MSMs), a general clas...
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02/16/2020
Regression-based Negative Control of Homophily in Dyadic Peer Effect Analysis
A prominent threat to causal inference about peer effects over social ne...
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11/21/2019
A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity
There is a fast-growing literature on estimating optimal treatment regim...
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11/05/2019
Bias-aware model selection for machine learning of doubly robust functionals
While model selection is a well-studied topic in parametric and nonparam...
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07/27/2019
Multiply Robust Learning of the Average Treatment Effect with an Invalid Instrumental Variable
Instrumental variable (IV) methods have been widely used to identify cau...
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08/15/2018
A Confounding Bridge Approach for Double Negative Control Inference on Causal Effects (Supplement and Sample Codes are included)
Unmeasured confounding is a key challenge for causal inference. Negative...
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08/15/2018
A Confounding Bridge Approach for Double Negative Control Inference on Causal Effects
Unmeasured confounding is a key challenge for causal inference. Negative...
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08/14/2018
Multiply Robust Causal Inference With Double Negative Control Adjustment for Unmeasured Confounding
Unmeasured confounding is a threat to causal inference in observational ...
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07/14/2018
Learning Causal Hazard Ratio with Endogeneity
Cox's proportional hazards model is one of the most popular statistical ...
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04/16/2018
