Long-term causal effects via behavioral game theory
Open/View files
Date
2016
Author(s)
The Harvard community has made this article openly available. Please share how this access benefits you.
Citation
Toulis, Panagiotis and David C. Parkes. 2016. Long-term causal effects via behavioral game theory. In Proceedings of Advances in Neural Information Processing Systems 29 (NIPS 2016), Barcelona, Spain, December 5-10, 2016.
Abstract
Planned experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy. % One critical shortcoming of classical experimental methods, however, is that they typically do not take into account the dynamic nature of response to policy changes. For instance, in an experiment where we seek to understand the effects of a new ad pricing policy on auction revenue, agents may adapt their bidding in response to the experimental pricing changes. Thus, causal effects of the new pricing policy after such adaptation period, the {\em long-term causal effects}, are not captured by the classical methodology even though they clearly are more indicative of the value of the new policy. % Here, we formalize a framework to define and estimate long-term causal effects of policy changes in multiagent economies. Central to our approach is behavioral game theory, which we leverage to formulate the ignorability assumptions that are necessary for causal inference. Under such assumptions we estimate long-term causal effects through a latent space approach, where a behavioral model of how agents act conditional on their latent behaviors is combined with a temporal model of how behaviors evolve over time.
Terms of Use
This article is made available under the terms and conditions applicable to Open Access Policy Articles (OAP), as set forth at Terms of Service
Collection