841 citations · 1.1k across the 16 of their papers we have counts for
17 papers
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
Inioluwa Deborah Raji, Lydia T. Liu, Angela Zhou +27
Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings -…
Bridging Prediction and Intervention Problems in Social Systems
Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou +32
Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals…
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing
Jitao Wang, Chengchun Shi, John D. Piette +3
When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy m…
Position: The Causal Revolution Needs Scientific Pragmatism
Joshua Loftus
Causal models and methods have great promise, but their progress has been stalled. Proposals using causality get squeezed between two opposing worldviews. Scientific perfectionism-…
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,…
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…
Causal intersectionality for fair ranking
Ke Yang, Joshua R. Loftus, Julia Stoyanovich
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings. Rankings are used…
Causal Interventions for Fairness
Matt J. Kusner, Chris Russell, Joshua R. Loftus +1
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help pr…
Causal Reasoning for Algorithmic Fairness
Joshua R. Loftus, Chris Russell, Matt J. Kusner +1
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in…
Counterfactual Fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell +1
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In…
Selective inference after cross-validation
Joshua R. Loftus
This paper describes a method for performing inference on models chosen by cross-validation. When the test error being minimized in cross-validation is a residual sum of squares it…
Selective inference in regression models with groups of variables
Joshua R. Loftus, Jonathan E. Taylor
We provide a general mathematical framework for selective inference with supervised model selection procedures characterized by quadratic forms in the outcome variable. Forward ste…
Selective inference with unknown variance via the square-root LASSO
Xiaoying Tian, Joshua R. Loftus, Jonathan E. Taylor
There has been much recent work on inference after model selection when the noise level is known, however, is rarely known in practice and its estimation is difficult in high-d…
A significance test for forward stepwise model selection
Joshua R. Loftus, Jonathan E. Taylor
We apply the methods developed by Lockhart et al. (2013) and Taylor et al. (2013) on significance tests for penalized regression to forward stepwise model selection. A general fram…
Tests in adaptive regression via the Kac-Rice formula
Jonathan Taylor, Joshua Loftus, Ryan Tibshirani
We derive an exact p-value for testing a global null hypothesis in a general adaptive regression problem. The general approach uses the Kac-Rice formula, as described in (Adler & T…