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04/14/2023
Causal inference with a functional outcome
This paper presents methods to study the causal effect of a binary treat...
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01/27/2023
Convolutional neural networks for valid and efficient causal inference
Convolutional neural networks (CNN) have been successful in machine lear...
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11/30/2021
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Semiparametric inference about average causal effects from observational...
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05/06/2021
SDRcausal: an R package for causal inference based on sufficient dimension reduction
SDRcausal is a package that implements sufficient dimension reduction me...
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05/05/2021
The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters
Important advances have recently been achieved in developing procedures ...
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02/28/2021
Covariate balancing for causal inference on categorical and continuous treatments
We propose novel estimators for categorical and continuous treatments by...
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11/05/2018
Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect
When estimating the treatment effect in an observational study, we use a...
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03/23/2018
Robust semiparametric estimators: missing data and causal inference
Semiparametric inference with missing outcome data (including causal inf...
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12/01/2017
Causal inference taking into account unobserved confounding
Causal inference with observational data can be performed under an assum...
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11/28/2017
Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals
To estimate direct and indirect effects of an exposure on an outcome fro...
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10/12/2017