授業內容
Studies advanced econometric methodologies and causal machine learning algorithms used in a wide variety of applications in economics. In particular, this advanced seminar discusses the relevance of recent machine learning (ML) literature for economics and econometrics. We first discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literature. Then we discuss more specific methods from the ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.
授業の狙い
Cultivate knowledge and taste of writing decent econometric papers in the field of causal machine learning. In particular, students will be able to think about, as well as understand concepts and issues in causal machine learning techniques. Students will be also equipped with the facility with R-language programming and data handling.
Prerequisite
Q0LEM00901 Machine learning in economics
References
- Athey and Imbens (2019), Machine learning method that economists should know about.
- Chernozhukov, Hansen, and Spindler (2015), Valid post-selection and post-regularization inference: an elementary, general approach.
- Chernozhukov and et al. (2018), Double/debiased machine learning for treatment and structural parameters.
- Chernozhukov, Demirer, Duflo, and Fernandez-Val (2018), Generic machine learning inference on heterogeneous treatment effects in randomized experiments.
- Ahtey and Imbens (2016), Recursive partitioning for heterogeneous causal effects.
- Wager and Athey (2018), Estimation and inference of heterogeneous treatment effects using random forests.
- Athey, Tibshirani, and Wager (2019), Generalized random forests.
- Athey and Wager (2019), Estimating treatment effects with causal forests: an application.
- Kitagawa and Tetenov (2018), Who should be treated? empirical welfare maximization methods for treatment choice.
- Athey and Wager (2018), Efficient policy learning.
Jauer 