This course offers a unified introduction to the principles and methods of statistical modeling and causal inference – two areas essential to data analysis in economics. The first part of this course introduces learning theory and a number of modern machine learning methods used for pattern recognition and predictive modeling. The second part introduces the theory of causal inference and surveys frequently used econometric techniques for causal effect learning and program evaluation. Finally, we discuss structural estimation and offer a unified perspective on the use of reduced-form and structural econometric methods.
Throughout the course, methods are demonstrated with applications to actual and simulated problems in various fields of applied economics, such as labor economics, industrial organization, finance, and marketing. The course spans the fields of econometrics, statistics, and computer science. Although the focus is on the analysis of economic data, the theories and the tools presented should be useful for a wide range of research areas in business and the social sciences.
For more information, see the course website at https://jiamingmao.github.io/data-analysis/
The goal of this course is to equip students with both a solid theoretical foundation, and the tools they need to conduct hands-on empirical research using state-of-the-art technology. The lecture materials are written to be both deep conceptually and easy to follow technically. Throughout the course, methods are demonstrated with applications to actual and simulated problems in various fields of applied economics, such as labor economics, industrial organization, finance, and marketing. Students will learn how to explore and analyze large high-dimensional datasets, choose appropriate methods for answering different types of queries, including associational, causal, and counterfactual, as well as gaining valuable computational skills.
0.Introduction
1.5 Introduction to Causal Inference (Part II)
1.1 Data Analysis for Economics
1.4 Introduction to Causal Inference (Part I)
1.3 Introduction to Statistical Learning (Part II)
1.Course Introduction
1.6 Introduction to Structural Estimation
1.2 Introduction to Statistical Learning (Part I)
2.Foundations of Statistical Learning
2.12 Generative Models and Scientific Models
2.4 Finite Learning Model
2.5 VC Analysis
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2.10 Decision Theory
2.2 The Feasibility of Learning:
2.1 The Statistical Learning Problem
2.8 Information theory, Entropy and KL Divergence
2.6 Training and Testing
2.3 Hoeffding’s Inequality
2.9 Probabilistic Models and Maximum Likelihood
2.7 Error Analysis and the Bias-Variance Tradeoff
2.11 Discriminative Models
Homework(Chapter 2)
3.Regression
3.3 The Bootstrap Method
3.10 Generalization Issues
3.6 Maximum Likelihood Estimation
3.2 Asymptotic Inference and Hypothesis Testing
3.9 Generalized Additive Models
3.4 Log Linear and Log-log Models
3.7 Moving Beyond Linearity- Polynomial Models, Piecewise Constant Models, Linear Basis Function Models
3.5 Regression Diagnostics
3.1 The OLS Estimator
3.8 Regression Splines
Homework(Chapter3)
4.The Truth About P-Values
4.5 Data Snooping
4.4 P-Hacking and Multiple Testing
4.3 Publication Bias
4.1 Understanding Hypothesis Testing
4.2 Understanding P-Values
5.Classification and Discrete Choice Models
5.6 Classification Errors & Decision Theory (Part II)
5.7 Similarity-Based Methods
5.10 Discrete Choice Models and the Random Utility Framework
5.13 Multinomial Probit
5.3 Loss Functions for Classification
5.12 Probit Regression-
5.8 Multinomial Logistic Regression (Part I)
5.14 Logistic Regression as a Discrete Choice Model
5.15 Applications
5.9 Independence of Irrelevant Alternatives
5.5 Classification Errors & Decision Theory
5.2 Logistic Regression
5.1 Linear Probability Model
5.4 Generalized Linear Models
5.11 Multinomial Logistic Regression (Part II)-
6.Model Selection and Regularization
6.1 Model Assessment
6.6 Exploring the Bias And Variance Trade-off
6.9 Multi-Stage Lasso
6.5 Forward Stepwise Selection
6.2 Model Selection
6.10 Post Selection Inferences
6.4 Information Criteria
6.11 Shrinkage and Regularization
6.3 Cross Validation
6.7 Ridge Regression
6.8 The Lasso
7.Decision Trees and Ensemble Methods
7.8 Decision Tree and Random Forest- A Summary
7.11 Simulations
7.3 Classification Tree
7.5 Bagging
7.9 Boosting for Regression
7.7 Ensemble Methods
7.2 Regression Tree
7.12 Applications
7.6 Random Forest
7.1 Decision Tree
7.4 The Pros and Cons of Decision Trees
7.10 Boosting for Classification
8. Foundations of Causal Inference
8.25 Structural Models and Structural Estimation
8.12 Identifiability of Causal Effects
8.14 Causal Effect Learning- Identification and Estimation
8.19 The Back-door Criterion- Review
8.21 Instrumental Variables
8.5 Randomized Experiments
8.17 The Backdoor Criterion
8.23 Causal Mechanism Learning
8.7 Toward a Deeper Understanding of Causal Effects
8.18 Causal Effect Learning from Observational Data- Review
8.6 The Experimental Ideal and Its Limitations
8.24 Connection To Traditional Econometrics-
8.9 Association and Independence Relations in Causal Diagrams
8.8 Causal Graphical Model
8.20 The Front-door Criterion
8.3 Treatment Effects
8.15 Causal Effect Learning from Randomized Experiments
8.16 Causal Effect Learning from Observational Data
8.2 The Potential Outcomes Framework
8.11 Directed Acyclic Graphs (DAGs) & Baysian Networks
8.13 Intervention in Causal Diagrams
8.10 Confounding
8.22 Sample Selection
8.4 Self-Selection
8.1 Introduction
9. Regression For Causal Inference
9.4 How Many Variables Should Be Included
9.2 Causal Effect Estimation under Sufficient Control for Confounding
9.3 Examples
9.1 The Disjunctive Cause Criterion
You are expected to have familiarity with basic econometrics, statistics, and probability theory, as well as at least one programming/statistical computing language. We provide ample data analysis problems for you to work through in this course. The course lectures are written in R.
Undergraduate
Angrist, J. D. and J. Pischke. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
Cameron, A. C. and P. K. Trivedi. (2010). Microeconometrics using Stata (Revised ed.). Stata Press.
Hernán, M. A. and J. M. Robins (2019). Causal Inference. CRC Press.
James, G., D. Witten, T. Hastie, and R. Tibshirani. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
Morgan, S. L. and C. Winship. (2007). Counterfactuals and Causal Inference: Methods and Principles for Social Research.
Graduate
Abu-Mostafa, Y. S., M. Magdon-Ismail, and H. Lin. (2012). Learning from Data. AMLBook.
Cameron, A. C. and P. K. Trivedi. (2005). Microeconometrics: Methods and Applications. Cambridge University Press.
Hastie, T., R. Tibshirani, and J. Friedmand. (2008). The Elements of Statistical Learning (2nd ed.). Springer.
Pearl, J. (2009). Causality: Models, Reasoning and Inference (2nd ed.). Cambridge University Press.
Wooldridge, J. M. (2011). Econometric Analysis of Cross Section and Panel Data (2nd ed.). The MIT Press.
Assistant Professor
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