Causal Inference from Competing Treatments

Many applications of RCTs involve the presence of multiple treatment administrators—from field experiments to online advertising—that compete for the subjects’ attention. In the face of competition, estimating a causal effect becomes difficult, as the position at which a subject sees a treatment influences their response, and thus the treatment effect. In this talk I will present a recent paper in which we build a game-theoretic model of agents who wish to estimate causal effects in the presence of competition, through a bidding system and a utility function that minimizes estimation error. The main technical result establishes an approximation with a tractable objective that maximizes the sample value obtained through strategically allocating budget on subjects. Conceptually, this work successfully combines elements from causal inference and game theory to shed light on the equilibrium behavior of experimentation under competition. We'll discuss societal implications of experimentation derived from our results, from policy evaluation to fairness in marketing campaigns. This work is joint with Vivian Y. Nastl and Moritz Hardt and will be presented at ICML'24.

Bio: Ana-Andreea Stoica is a Research Group Leader in the Social Foundations of Computation group at the Max Planck Institute for Intelligent Systems, Tuebingen. Her work focuses on social foundations in networked systems, such as algorithm design, fairness and inequality evaluation methods, and causal inference under interference. From recommendation algorithms to the way information spreads in networks, Ana is particularly interested in studying the effect of algorithms on people's sense of community and access to information and opportunities. Ana holds a Ph.D. from Columbia University and a B.A. in Mathematics from Princeton University. Since 2019, she has been co-organizing the Mechanism Design for Social Good initiative and is a co-founder of ACM conference series on Equity and Access in Algorithms, Mechanisms, and Optimization.