An Honest Approach to Parallel Trends
Standard approaches for causal inference in difference-in-differences and event-study designs are valid only under the assumption of parallel trends. Researchers are typically unsure whether the…
A More Credible Approach to Parallel Trends ∗
This paper proposes tools for robust inference in diﬀerence-in-diﬀerences and event-study designs where the parallel trends assumption may be violated. Instead of re-quiring that parallel trends…
An Economic Approach to Regulating Algorithms
- Ashesh Rambachan, J. Kleinberg, S. Mullainathan, J. Ludwig
- EconomicsSSRN Electronic Journal
- 1 May 2020
This work builds a model that allows the training data to exhibit a wide range of "biases" and finds two striking irrelevance results, which provide a baseline set of assumptions that must be altered to generate different conclusions on algorithmic bias.
Characterizing Fairness Over the Set of Good Models Under Selective Labels
- Amanda Coston, Ashesh Rambachan, A. Chouldechova
- Computer ScienceInternational Conference on Machine Learning
- 2 January 2021
A framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance, or “the set of good models,” is developed, which addresses the empirically relevant challenge of selectively labelled data in the setting where the selection decision and outcome are unconfounded given the observed data features.
Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function
Bojinov & Shephard (2019) defined potential outcome time series to nonparametrically measure dynamic causal effects in time series experiments. Four innovations are developed in this paper:…
Bias In, Bias Out? Evaluating the Folk Wisdom
- Ashesh Rambachan, J. Roth
- EconomicsSymposium on Foundations of Responsible Computing
- 18 September 2019
Whether a prediction algorithm reverses or inherits bias depends critically on how the decision-maker affects the training data as well as the label used in training.
Panel experiments and dynamic causal effects: A finite population perspective
- Iavor Bojinov, Ashesh Rambachan, N. Shephard
- Mathematics, EconomicsQuantitative Economics
- 22 March 2020
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define…
Identifying Prediction Mistakes in Observational Data*
- Ashesh Rambachan
Decision makers, such as doctors, judges, and managers, make consequential choices based on predictions of unknown outcomes. Do these decision makers make systematic prediction mistakes based on the…
Design-Based Uncertainty for Quasi-Experiments
Social scientists are often interested in estimating causal effects in settings where all units in the population are observed (e.g. all 50 US states). Design-based approaches, which view the…
A Nonparametric Dynamic Causal Model for Macroeconometrics
A nonparametric framework for quantifying dynamic causal effects in macroeconometrics is used and it is shown that the structural vector moving average form is causally equivalent to a restricted potential outcome time series under the usual invertibility assumption.