Research
- Crime Categories
- Murder Circumstances
- Charges
- Murder Numbers by SHR
- Definitions of Murder
- Crime Literature
- Other Literature
- Seminars
- Journal Ranking
- Laws
- Changes in Law and Reporting in Michigan
- Citation Guides
- Datasets
Writing
Methods
- BLP
- Econometrics Models
- Econometrics Tests
- Econometrics Resources
- Event Study Plots
- Metrics Literature
- Machine Learning
Python-related
- Python Basic Commands
- Pandas Imports and Exports
- Pandas Basic Commands
- Plotting in Python
- Python web scraping sample page
- Two Sample t Test in Python
- Modeling in Python
R-related
- R Basics
- R Statistics Basics
- RStudio Basics
- R Graphics
- R Programming
- Accessing MySQL Databases from R
Latex-related
Stata-related
SQL
Github
Linux-related
Conda-related
AWS-related
Webscraping
Interview Prep
Other
Interview Prep
Causal Inference
RD
Assumptions:
- Continuity: probability of treatment is constant
Effects:
- LATE
Procedure:
- Center variable at 0
- Regress
- regression with variable that denotes cutoff
- Local linear nonparametric regression ()
- Regress with kernel
- Fuzzy RD
- Two-stage least squares regression
AB Testing
Comparing two samples
or
reject null.
Other
Chi-squared test
- Usage:
- Test for correlation between categorical variables
- Test for variance
Binomial
- Assumptions:
- iid
Product sense
Step 1: ask questions to understand the problem
Step 2: provide a structure
Step 3: identify the users and customers
Step 4: What are the use cases? Why are they using this product? What are their goals?
Step 5: How well is the current product doing for their use cases? Are there obvious weak spots?
Step 6: What features or changes would improve those weak spots?
Step 7: Wrap things up