Causal analysis simulations
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Updated
Jan 31, 2023 - Jupyter Notebook
Causal analysis simulations
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.
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.
The main objectif is to conduct an exploratory data analysis on the data & communicate useful insights.
Notebooks for a short course on urban econometrics, also covering causal inference methods such as experiments, matching, difference in difference, instrumental variables, etc.
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
R notebooks associated with the review article: "Generalizing a causal effect: sensitivity analysis and missing covariates"
R notebooks associated with the review article: "Causal inference methods for combining randomized controlled trials and observational studies: a review"
python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper
Colab notebooks exploring different Machine Learning topics.
Notebooks of Python and R code which illustrates basic causal inference using simulated data
Notebooks for Applied Causal Inference Powered by ML and AI
Causal Inference Crash Course for Scientists - contains slides and Jupyter notebooks
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