This repo is my effor to collect and gather all the resources, including notebooks/code that I've learnt or used in the courses I've taken or the proejcts I've done so far.
- Brady Neal Causal Inference
- Paul Hünermund Causal Data Science
- Michael E. Sobel Causal Inference in 2 parts
- PGM
- Online Causal Inference Seminar (Videos)
- Causal Data Science (CDSM)
- Causality by Adam Kelleher
- CausalML by Uber
- EconML by Microsoft
- Causale2e by Daniel Grünbaum
- PyCausal
- DoWhy by Microsoft
- CausalNex by McKinsey
- [A collections of datasets by Ruocheng Guo] (https://github.com/rguo12/awesome-causality-data)
- Journal of Caual Inference - Link
- Experience in applying at least one of the following in your research: survey methodology (e.g., bias correction, sampling and design), quantitative research methods or statistical analysis, regression modeling, causal inference with a survey outcome, shrinkage and regularization, designing and analyzing longitudinal panel surveys, behavioral data analysis
- Experience with data analysis using tools such as R or Python and with SQL
- Experience initiating and driving research projects to completion with minimal guidance
- Experience communicating the results of analyses
- Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
Preferred Qualification
- Experience with large data sets and distributed computing (Hive/Hadoop)
- Experience with Bayesian statistical models
- Experience with primary data collection
- Experience with field experiments, experimental design, missing data, survey sampling, and/or panel data
- Experience with observational causal inference (e.g., regression adjustment, matching, propensity score stratification), or quasi-experimental methods (e.g., instrumental variables, regression discontinuity, interrupted time series)
- Experience with bandit optimization, adaptive experimentation, and/or Gaussian processes