6 citations · 13 across the 9 of their papers we have counts for
10 papers
Unit-level aggregation breaks coverage in causal foundation models
Gabe Schulman, Lucius E. J. Bynum, Yoav Wald
Causal foundation models (CFMs) show promising results for treatment-effect estimation, with unit-level estimates that are competitive on standard benchmarks. Many causal analyses,…
Computational Identifiability
Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho
Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available. In causal identificat…
Black Box Causal Inference: Effect Estimation via Meta Prediction
Lucius E. J. Bynum, Aahlad Manas Puli, Diego Herrero-Quevedo +4
Causal inference and the estimation of causal effects plays a central role in decision-making across many areas, including healthcare and economics. Estimating causal effects typic…
Language Models as Causal Effect Generators
Lucius E. J. Bynum, Kyunghyun Cho
In this work, we present sequence-driven structural causal models (SD-SCMs), a framework for specifying causal models with user-defined structure and language-model-defined mechani…
A New Paradigm for Counterfactual Reasoning in Fairness and Recourse
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
Counterfactuals and counterfactual reasoning underpin numerous techniques for auditing and understanding artificial intelligence (AI) systems. The traditional paradigm for counterf…
Causal Dependence Plots
Joshua R. Loftus, Lucius E. J. Bynum, Sakina Hansen
Explaining artificial intelligence or machine learning models is increasingly important. To use such data-driven systems wisely we must understand how they interact with the world,…
The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
Andrew Bell, Lucius Bynum, Nazarii Drushchak +3
The ``impossibility theorem'' -- which is considered foundational in algorithmic fairness literature -- asserts that there must be trade-offs between common notions of fairness and…
Counterfactuals for the Future
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the…
Disaggregated Interventions to Reduce Inequality
Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich
A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of…
Argumentative Topology: Finding Loop(holes) in Logic
Sarah Tymochko, Zachary New, Lucius Bynum +4
Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the…