1.0 Introduction In the past two blogs of this series we’ve been discussing causal estimation, a very important subject in data science, we delved into causal estimation using regression method and propensity score matching. Now, let’s venture into the world of instrumental variable analysis—a powerful method for unearthing causal relationships from observational data. Let us look at the structure of this blog. 2.0 Structure 3.0 Instrument Variables – An introduction Let us start the explanation on the instrument variable analysis with an example. We all know education is important, but...…1.0 Introduction In the past two blogs of this series we’ve been discussing causal estimation, a very important subject in data science, we delved into causal estimation using regression method and propensity score matching. Now, let’s venture into the world of instrumental variable analysis—a powerful method for unearthing causal relationships from observational data. Let us look at the structure of this blog. 2.0 Structure 3.0 Instrument Variables – An introduction Let us start the explanation on the instrument variable analysis with an example. We all know education is important, but...WW…

In our last blog, we covered the basics of causal analysis, starting from defining problems to creating simulated data. We explored key concepts like back door, front door, and instrumental variables for handling complex causal relationships. Now, we’re taking the next step, focusing on estimation methods, understanding causal effects, and diving into the world of propensity score estimation. Join us as we delve deeper into causal analysis, applying these concepts to Member Loyalty Programs. In this part of the series, we’ll be tackling the following: Structure 1.0 Causal estimation Now...…In our last blog, we covered the basics of causal analysis, starting from defining problems to creating simulated data. We explored key concepts like back door, front door, and instrumental variables for handling complex causal relationships. Now, we’re taking the next step, focusing on estimation methods, understanding causal effects, and diving into the world of propensity score estimation. Join us as we delve deeper into causal analysis, applying these concepts to Member Loyalty Programs. In this part of the series, we’ll be tackling the following: Structure 1.0 Causal estimation Now...WW…

Image Source : Google images 1.0 Introduction In the world of data science, uncovering cause-and-effect relationships from observational data can be quite challenging. In our earlier discussion, we dived into using linear regression for causal estimation, a fundamental statistical method. In this post, we introduce propensity score matching. This technique builds on linear regression foundations, aiming to enhance our grasp of cause-and-effect dynamics. Propensity score matching is crafted to deal with confounding variables, offering a refined perspective on causal relationships. Throughout...…Image Source : Google images 1.0 Introduction In the world of data science, uncovering cause-and-effect relationships from observational data can be quite challenging. In our earlier discussion, we dived into using linear regression for causal estimation, a fundamental statistical method. In this post, we introduce propensity score matching. This technique builds on linear regression foundations, aiming to enhance our grasp of cause-and-effect dynamics. Propensity score matching is crafted to deal with confounding variables, offering a refined perspective on causal relationships. Throughout...WW…

Let us embark on an insightful exploration into causal effects within machine learning and data science. In this blog, we will delve into causal estimation methods, emphasizing their importance in addressing complex business problems and decision-making processes.This comprehensive guide aims to shed light on crucial methodologies that unlock deeper insights in Causal AI. This blog is in continuation to our earlier blogs on causal analysis dealing with the following topics Estimating the effect on loyalty program Back door, front door and instrument analysis in causal AI Causal graphs...…Let us embark on an insightful exploration into causal effects within machine learning and data science. In this blog, we will delve into causal estimation methods, emphasizing their importance in addressing complex business problems and decision-making processes.This comprehensive guide aims to shed light on crucial methodologies that unlock deeper insights in Causal AI. This blog is in continuation to our earlier blogs on causal analysis dealing with the following topics Estimating the effect on loyalty program Back door, front door and instrument analysis in causal AI Causal graphs...WW…

Structure Introduction to causal analysis Implementation of causal analysis using DoWhy Business context – Member loyalty program Causal analysis process in a nutshell Process 1 : Defining the problem statement Process 2 : Data preparation through simulation Defining treatment and control for membership program Creation of simulated data for causal analysis Process 3 : Defining the causal graph structure Process 4 : Causal identification Conclusion – Processes to be implemented in Part II of the series Introduction In this series, we’re introducing a paradigm that’s very important in the...…Structure Introduction to causal analysis Implementation of causal analysis using DoWhy Business context – Member loyalty program Causal analysis process in a nutshell Process 1 : Defining the problem statement Process 2 : Data preparation through simulation Defining treatment and control for membership program Creation of simulated data for causal analysis Process 3 : Defining the causal graph structure Process 4 : Causal identification Conclusion – Processes to be implemented in Part II of the series Introduction In this series, we’re introducing a paradigm that’s very important in the...WW…