ECON 570 Syllabus 2020 Fall PDF

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ECON 570: Big Data Econometrics Machine Learning and Causal Inference Laurence Wong University of Southern California Fall 2020 (4 units)

Course Description This course provides an introduction to the theory and practice of causal econometrics in modern settings of large-scale data. Major algorithms from machine learning will be introduced as tools for statistical pattern recognition as well as powerful aids to methods for identifying causal effects. While learnings from theory will be emphasized, the course will be focused on methodology and applications rather than rigorous proofs of theorems.

Learning Objectives By the end of the course, students should be able to: ● Have a good sense of the computational complexity of different estimators and algorithms. ● Apply common machine learning algorithms on real data sets using a scientific computing software language. ● Develop and assess empirical strategies for identifying causal effects in both experimental and observational settings. ● Combine the tools of machine learning and causal inference to tackle empirical questions in big data settings.

Prerequisites Necessary background for this course are calculus (at the level of MATH 226), linear algebra (at the level of MATH 225), and graduate-level econometrics (at the level of ECON 513). Formal training in causal inference and machine learning are not assumed, though some prior exposure is helpful.

Supplementary Materials All required material will be covered by lecture notes. For students interested in more in-depth expositions, the following textbooks are recommended. Relevant chapters are listed in the course schedule. 1. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning . Springer. [HTF]

2. Bishop, C. M. (2007). Pattern Recognition and Machine Learning . Springer. [B] 3. Imbens, G. W., and Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction . Cambridge University Press. [IR] 4. Angrist, J. D., and Pischke, J. (2009). Mostly Harmless Econometrics . Princeton University Press. [AP]

Course Grading 3 Problem Sets: 60% Regular problem sets will be given to reinforce the concepts taught in class, as well as offer an opportunity for students to code and implement the algorithms covered. Students are encouraged to work in groups, but each person must turn in their own copy. Assessment will be based on whether the right approaches were used and whether the right solutions were obtained. Due dates for the assignments are September 18, October 9, and November 6.

1 Empirical Project: 40% For the empirical project, students are expected to work in groups (maximum of four) and apply their learnings to a real data set to tackle an empirical problem that interests them. Each group must submit a short write-up (6 pages max) that summarizes the analysis, and computer code that reproduces the quantitative results. Assessment will be based on how appropriately the quantitative tools were applied. Due date for this project is November 20.

Software Python will be the de facto  programming language used in this course. Lecture notes will contain Python code snippets, TA sessions will include Python instructions, and any code in problem set solutions will be in Python. However, if students insist on doing their problem sets and project in another language like R, they may. No prior exposure to Python is assumed. In fact the TA sessions and problem sets should guide students through the learning process. Prior exposure to some  programming language is, however, extremely helpful.

Course Schedule Week

Topics

1

Course overview; Computational complexity

2

Unsupervised learning: Dimensionality reduction

Readings

HTF 14

3

Unsupervised learning: Matrix factorization; Embeddings

HTF 14

4

Unsupervised learning: Cluster analysis

5

Supervised learning: Model selection; Regularized regression

HTF 7, 5

6

Supervised learning: Tree-based methods

HTF 9

7

Supervised learning: Bagging; Boosting

HTF 15, 10

8

Introduction to Deep Learning

9

Causal inference: Unconfoundedness; Propensity score methods

IR 12-17

10

Causal inference: Matching estimators

IR 18

11

Causal inference: Instrumental variables; Regression discontinuity

IR 23, 24; AP 6

12

ML econometrics: Flexible controls; heterogeneous treatment effects

13

Buffer

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