Timezone: »
Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals to improve a desired quality. Key to both accounts is a cost function that dictates which adaptations are rational to undertake. In this work, we develop a causal framework for strategic adaptation. Our causal perspective clearly distinguishes between gaming and improvement and reveals an important obstacle to incentive design. We prove any procedure for designing classifiers that incentivize improvement must inevitably solve a non-trivial causal inference problem. We show a similar result holds for designing cost functions that satisfy the requirements of previous work. With the benefit of hindsight, our results show much of the prior work on strategic classification is causal modeling in disguise.
Author Information
John Miller (University of California, Berkeley)
Smitha Milli (UC Berkeley)
Moritz Hardt (University of California, Berkeley)
More from the Same Authors
-
2021 : Causal Inference Struggles with Agency on Online Platforms »
Smitha Milli · Luca Belli · Moritz Hardt -
2021 : Smitha Milli -- Causal Inference Struggles with Agency on Online Platforms »
Smitha Milli -
2021 Poster: Outside the Echo Chamber: Optimizing the Performative Risk »
John Miller · Juan Perdomo · Tijana Zrnic -
2021 Poster: Alternative Microfoundations for Strategic Classification »
Meena Jagadeesan · Celestine Mendler-Dünner · Moritz Hardt -
2021 Poster: Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization »
John Miller · Rohan Taori · Aditi Raghunathan · Shiori Sagawa · Pang Wei Koh · Vaishaal Shankar · Percy Liang · Yair Carmon · Ludwig Schmidt -
2021 Spotlight: Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization »
John Miller · Rohan Taori · Aditi Raghunathan · Shiori Sagawa · Pang Wei Koh · Vaishaal Shankar · Percy Liang · Yair Carmon · Ludwig Schmidt -
2021 Spotlight: Outside the Echo Chamber: Optimizing the Performative Risk »
John Miller · Juan Perdomo · Tijana Zrnic -
2021 Spotlight: Alternative Microfoundations for Strategic Classification »
Meena Jagadeesan · Celestine Mendler-Dünner · Moritz Hardt -
2020 Workshop: Participatory Approaches to Machine Learning »
Angela Zhou · David Madras · Deborah Raji · Smitha Milli · Bogdan Kulynych · Richard Zemel -
2020 : Opening remarks »
Deborah Raji · Angela Zhou · David Madras · Smitha Milli · Bogdan Kulynych -
2020 Poster: Performative Prediction »
Juan Perdomo · Tijana Zrnic · Celestine Mendler-Dünner · Moritz Hardt -
2020 Poster: Test-Time Training with Self-Supervision for Generalization under Distribution Shifts »
Yu Sun · Xiaolong Wang · Zhuang Liu · John Miller · Alexei Efros · Moritz Hardt -
2020 Poster: Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning »
Esther Rolf · Max Simchowitz · Sarah Dean · Lydia T. Liu · Daniel Bjorkegren · Moritz Hardt · Joshua Blumenstock -
2020 Poster: The Effect of Natural Distribution Shift on Question Answering Models »
John Miller · Karl Krauth · Benjamin Recht · Ludwig Schmidt -
2019 Poster: Natural Analysts in Adaptive Data Analysis »
Tijana Zrnic · Moritz Hardt -
2019 Poster: The Implicit Fairness Criterion of Unconstrained Learning »
Lydia T. Liu · Max Simchowitz · Moritz Hardt -
2019 Oral: The Implicit Fairness Criterion of Unconstrained Learning »
Lydia T. Liu · Max Simchowitz · Moritz Hardt -
2019 Oral: Natural Analysts in Adaptive Data Analysis »
Tijana Zrnic · Moritz Hardt -
2018 Poster: Delayed Impact of Fair Machine Learning »
Lydia T. Liu · Sarah Dean · Esther Rolf · Max Simchowitz · Moritz Hardt -
2018 Oral: Delayed Impact of Fair Machine Learning »
Lydia T. Liu · Sarah Dean · Esther Rolf · Max Simchowitz · Moritz Hardt -
2017 Workshop: Reliable Machine Learning in the Wild »
Dylan Hadfield-Menell · Jacob Steinhardt · Adrian Weller · Smitha Milli