Below are links to some resources you may find useful.
Initial construction of webpage still in process. |
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Learning to Program
- Code Academy offers programming courses in many languages, including Python.
- Accounting Coding Camp offers courses for empirical accounting and finance researchers in SAS, STATA, and Python.
- Stack Overflow is a discussion board website where answers to specific programming questions can be found.
Useful Python Packages
- scikit-learn - Machine Learning in Python
- gensim - topic modeling
- New to topic modeling? Check David Blei's webpage. I would suggest reading his general introduction first.
Causal Inference
- Imbens and Rubin (2015) - Causal Inference for Statistics, Social, and Biomedical Sciences
- Morgan and Winship (2014) - Counterfactuals and Causal Inference
- Pearl and Mackenzie (2018) - The Book of Why
- Here is Pearl's own website about the book.
Econometrics
- Angrist and Pischke (2015) - Mastering 'Metrics: The Path from Cause to Effect
- A good primer into econometrics and causal inference. Written for intuition rather than for theoretical underpinnings.
- Angrist discusses the book and related material on an episode of Econtalk.
- Angrist and Pischke (2009) - Mostly Harmless Econometrics: An Empiricist's Companion
- More theoretical underpinnings than Mostly Harmless, but focuses and what empiricsts should care about. also gets into RDD and quantile regression.
Networks Science
- Textbooks
- A First Course in Network Science (2020) by Filippo Menczer, Santo Fortunato, and Clayton A. Davis
- Network Science (2016) by Albert-Lászó Barabási
- Networks (2010, 1st Ed.; 2018, 2nd Ed.) by Mark Newman
- Complex Network Analysis in Python (2018) by Dmitry Zinoviev
- Multilayer Networks: Structure and Function (2018) by Ginestra Bianconi
- Inferential Network Analysis (Analytical Methods for Social Research) (2021) by Skyler J. Cranmer, Bruce A. Desmarais, and Jason W. Morgan
- Software
- Gephi is a network visualization and exploration software (open-source and free)