Notebooks for Applied Causal Inference Powered by ML and AI
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Updated
May 14, 2024 - Jupyter Notebook
Notebooks for Applied Causal Inference Powered by ML and AI
Causality is a fundamental concept that seeks to determine the relationships between events, particularly to discern whether one event is the result of another. In this notebook we will establish causality by determining whether a change in one variable causes a change in another variable.
Explore the impact of discounts and tech support on revenue through Causal ML models. This repo provides an analysis notebook, data, and a guide on leveraging machine learning for strategic business decisions.
Causal Inference Crash Course for Scientists - contains slides and Jupyter notebooks
Notebooks for a short course on urban econometrics, also covering causal inference methods such as experiments, matching, difference in difference, instrumental variables, etc.
python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper
Causal analysis simulations
R notebooks associated with the review article: "Generalizing a causal effect: sensitivity analysis and missing covariates"
Colab notebooks exploring different Machine Learning topics.
The main objectif is to conduct an exploratory data analysis on the data & communicate useful insights.
R notebooks associated with the review article: "Causal inference methods for combining randomized controlled trials and observational studies: a review"
Notebooks (mostly R but some PyMC3) covering Prof Richard McElreath's Statistical Rethinking 2 book (draft version up to 26th Sept 2019) and Homeworks from his winter 2019 lecture course
Notebooks of Python and R code which illustrates basic causal inference using simulated data
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