Exploring Causality with DoWhy

Perform Causal Inference Using Python Step by Step with DoWhy

We began exploring causality by uncovering the risks of relying solely on raw data highlighted in “The Traps of Blindly Trusting Data: Simpson’s Paradox.” We explored through “An Intuitive Introduction to Causal Inference” and shed light on fundamental concepts using “Visualizing Causality Using Causal Graphs.” Our discussion extended as we examined the effects of biases in “How Biases Impact Causal Inference” and then outlined effective strategies to mitigate these biases in “How to Control Causal Inference Bias.” We concluded by detailing “Essential Steps to Build the Structure Behind Cause and Effect through a Causal DAG,” laying the foundation for rigorous causal analysis. Each step of this journey brought us closer to mastering the art of causal inference using the DoWhy framework in Python.

This article explains

• Overview and the challenges of causal inference in observational data.
• How DoWhy library addresses the challenges of causal analysis?
• Steps for implementing the DoWhy Library with a detailed explanation
• Clear interpretation of the outputs from the DoWhy library.

Causal Inference Overview

Causal inference empowers us to explore and identify the reasons behind events and assess interventions for future improvements. By analyzing cause-and-effect…