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09/03/2023
Diagnosing the role of observable distribution shift in scientific replications
Many researchers have identified distribution shift as a likely contribu...
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06/05/2023
Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models
Algorithmic assignment of refugees and asylum seekers to locations withi...
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11/18/2022
Modular regression
This paper develops a new framework, called modular regression, to utili...
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09/19/2022
Distributionally robust and generalizable inference
We discuss recently developed methods that quantify the stability and ge...
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04/27/2022
On randomization inference after inexact Mahalanobis matching
In observational causal inference, matched-pairs studies are often analy...
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02/24/2022
Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty
During data analysis, analysts often have to make seemingly arbitrary de...
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06/06/2021
Causal aggregation: estimation and inference of causal effects by constraint-based data fusion
Randomized experiments are the gold standard for causal inference. In ex...
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05/07/2021
The r-value: evaluating stability with respect to distributional shifts
Common statistical measures of uncertainty like p-values and confidence ...
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08/29/2020
Model selection for estimation of causal parameters
A popular technique for selecting and tuning machine learning estimators...
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07/30/2019
Incremental causal effects
This is a draft. The ignorability assumption is a key assumption in caus...
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01/18/2018
Anchor regression: heterogeneous data meets causality
This is a preliminary draft of "Anchor regression: heterogeneous data me...
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06/08/2015
