Zajonc on "Bayesian Inference for Dynamic Treatment Regimes" 

 We hope you will join us this Wednesday, March 10th at the Applied Statistics workshop when we will be happy to have  Tristan Zajonc  (Harvard Kennedy School). Details and an abstract are below. A light lunch will be served. Thanks! 

 "Bayesian Inference for Dynamic Treatment Regimes" 
Tristan Zajonc 
Harvard Kennedy School 
March 10th, 2010, 12 noon 
K354 CGIS Knafel (1737 Cambridge St) 

 Abstract: 
 Policies in health, education, and economics often unfold sequentially and adapt to developing conditions. Doctors treat patients over time depending on their prognosis, educators assign students to courses given their past performance, and governments design social insurance programs to address dynamic needs and incentives. I present the Bayesian perspective on causal inference and optimal treatment choice for these types of adaptive policies or dynamic treatment regimes. The key empirical difficulty is dynamic selection into treatment: intermediate outcomes are simultaneously pre-treatment confounders and post-treatment outcomes, causing standard program evaluation methods to fail. Once properly formulated, however, sequential selection into treatment on past observables poses no unique difficulty for model-based inference, and analysis proceeds equivalently to a full-information analysis under complete randomization. I consider optimal treatment choice as a Bayesian decision problem. Given data on past treated and untreated units, analysts propose treatment rules for future units to maximize a policymaker's objective function. When policymaker's have multidimensional preferences, the approach can estimate the set of feasible outcomes or the tradeoff between equity and efficiency. I demonstrate these methods through an application to optimal student tracking in ninth and tenth grade mathematics. An easy to implement optimal dynamic tracking regime increases tenth grade mathematics achievement 0.1 standard deviations above the status quo, with no corresponding increase in inequality. The proposed methods provide a flexible and principled approach to causal inference for sequential treatments and optimal treatment choice under uncertainty.