Bias In, Bias Out? Evaluating the Folk Wisdom. - Ashesh Rambachan; Jonathan Roth (FORC, 2020)

Characterizing Fairness Over the Set of Good Models Under Selective Labels. - Amanda Coston; Ashesh Rambachan; Alexandra Chouldechova (ICML, 2021)

Counterfactual Risk Assessments under Unmeasured Confounding. - Ashesh Rambachan; Amanda Coston; Edward H. Kennedy (CoRR, 2022)

Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function. - Keyon Vafa; Ashesh Rambachan; Sendhil Mullainathan (ICML, 2024)

Evaluating the World Model Implicit in a Generative Model. - Keyon Vafa; Justin Y. Chen; Ashesh Rambachan; Jon M. Kleinberg; Sendhil Mullainathan (NeurIPS, 2024)

Program Evaluation with Remotely Sensed Outcomes. - Ashesh Rambachan; Rahul Singh; Davide Viviano (CoRR, 2024)

Large Language Models: An Applied Econometric Framework. - Jens Ludwig; Sendhil Mullainathan; Ashesh Rambachan (CoRR, 2024)

What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models. - Keyon Vafa; Peter G. Chang; Ashesh Rambachan; Sendhil Mullainathan (CoRR, 2025)