430 citations · 856 across the 28 of their papers we have counts for
8 papers · 1 filter
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding
Jiajing Zheng, Alexander D'Amour, Alexander Franks
In causal estimation problems, the parameter of interest is often only partially identified, implying that the parameter cannot be recovered exactly, even with infinite data. Here,…
The MultiBERTs: BERT Reproductions for Robustness Analysis
Thibault Sellam, Steve Yadlowsky, Jason Wei +9
Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the parti…
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
Victor Veitch, Alexander D'Amour, Steve Yadlowsky +1
Informally, a 'spurious correlation' is the dependence of a model on some aspect of the input data that an analyst thinks shouldn't matter. In machine learning, these have a know-i…
Causally motivated Shortcut Removal Using Auxiliary Labels
Maggie Makar, Ben Packer, Dan Moldovan +3
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning. We study a flexible, causally-motiv…
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
Alexander D'Amour, Alexander Franks
A key condition for obtaining reliable estimates of the causal effect of a treatment is overlap (a.k.a. positivity): the distributions of the features used to perform causal adjust…
Revisiting Rashomon: A Comment on "The Two Cultures"
Alexander D'Amour
Here, I provide some reflections on Prof. Leo Breiman's "The Two Cultures" paper. I focus specifically on the phenomenon that Breiman dubbed the "Rashomon Effect", describing the s…