# Practical Python Causality: Econometrics for Data Science

In this tutorial, we'll learn Practical Python Causality: Econometrics for Data Science. Surely you will have a completely different view after reading our article.

### A data science introduction to econometrics with Python library: DoWhy, including a detailed code walkthrough of a case-study causality paper

Data scientists have a tendency to focus on descriptive and predictive analysis, but neglect causal analysis. Decision making, however, requires causal analysis, a fact well recognized by public health epidemiologists during this Covid-19 pandemic. Due to my background in biology, I had internalized the adage “_correlation does not equal causation_”, to such an extent that I studiously avoided all causal claims. Fortunately, my insatiable curiosity led me to the field of econometrics, which embraces causality and sets down a body of rigorous mathematics to facilitate causal analysis.

Recently, my interest in econometrics has been fueled by my regionally-focused consulting work on the Middle East and North Africa (MENA) with the World Bank. Specifically, a recently published paper in the journal _Political Science Research and Methods, _details a rare, causal impact evaluation of Israeli checkpoints on Palestinian employment outcomes (Abrahams, 2021). In “Hard traveling”, the author utilizes an instrumental variable in a cleverly designed experiment, highlighting the causal effects of road blockades deployed by the Israeli army during the ongoing occupation of Palestine. In this article, I explore causality through the lens of practical application, by replicating the results of this interesting paper in a practical Python tutorial.

Firstly, I make clear the distinction between machine learning and econometrics, a necessary step to convey the complexity and difficulty of causality. Next, I introduce the Python libraries used in this tutorial and discuss an econometric approach to causal analysis. Following that, I outline the experimental design described in the case-study paper, which flows directly into an intuitive walkthrough of the relevant equations. Lastly, using Python, I replicate the main results of the paper with a practical emphasis on code implementation.

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