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銆怱peaker銆慚onchen Yang, Indiana University Kelley School of Business锛孉ssistant Professor
銆怲opic銆慓enerating Instrumental Variables via Random Forest to Address Endogeneity due to Prediction (Measurement) Error in Data-Mined Variables
銆怲ime銆慣hursday, Nov. 29, 2018, 15:00-16:30pm
銆怴enue銆慠oom 513, Weilun Building, Tsinghua SEM
銆怢anguage銆慐nglish
銆怬rganizer銆慏epartment of Management Science and Engineering
銆怉bstract銆慣he practice of combining machine learning with econometric analysis is increasingly prevalent in both research and practice. In this work, we address one common example: the use of predictive modeling techniques to "mine" variables of interest from unstructured data, e.g., predicting sentiment from text, followed by the inclusion of those variables into an econometric framework, with the objective of making statistical inferences. We consider recent work, which highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses involving the predicted variables will suffer from biases and endogeneity deriving from measurement error. We propose a novel approach that mitigates these biases, leveraging instrumental variables generated from an ensemble learning technique known as the random forest. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, and which make "different" mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are close analogs for the relevance and exclusion requirements for a valid instrumental variable. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases, and its superior performance relative to an alternative method (simulation-extrapolation) proposed in prior work for addressing this problem.
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