Category: causal-inference
-
Causal ML for the Aspiring Data Scientist
Causal ML for the Aspiring Data Scientist An accessible introduction to causal inference and ML The post Causal ML for the Aspiring Data Scientist appeared first on Towards Data Science. Ross Lauterbach Go to original source
-
Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour
Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour Applying causal inference to measure the effect of product unavailability on retail sales at Carrefour The post Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour appeared first on Towards Data Science. Thanh Liêm NGUYEN Go…
-
Regression Discontinuity Design: How It Works and When to Use It
Regression Discontinuity Design: How It Works and When to Use It Regression Discontinuity Design: How It Works and When to Use It You’re an avid data scientist and experimenter. You know that randomisation is the summit of Mount Evidence Credibility, and you also know that when you can’t randomise, you resort to observational data and…
-
➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality
➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can, potentially, unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials. This post is targeted towards anyone making data driven…
-
Myths vs. Data: Does an Apple a Day Keep the Doctor Away?
Myths vs. Data: Does an Apple a Day Keep the Doctor Away? Introduction “Money can’t buy happiness.” “You can’t judge a book by its cover.” “An apple a day keeps the doctor away.” You’ve probably heard these sayings several times, but do they actually hold up when we look at the data? In this article series,…
-
Your Neural Network Can’t Explain This. TMLE to the Rescue!
Your Neural Network Can’t Explain This. TMLE to the Rescue! Targeted Maximum Likelihood Estimation (TMLE) helps you explain patterns where other techniques fall short Continue reading on Towards Data Science » Ari Joury, PhD Go to original source
-
Optimising Budgets With Marketing Mix Models In Python
Optimising Budgets With Marketing Mix Models In Python Part 3 of a hands-on guide to help you master MMM in pymc Photo by Towfiqu barbhuiya on Unsplash What is this series about? Welcome to part 3 of my series on marketing mix modelling (MMM), a hands-on guide to help you master MMM. Throughout this series, we’ll cover key…
-
Propensity-Score Matching Is the Bedrock of Causal Inference
Propensity-Score Matching Is the Bedrock of Causal Inference And how to get started with it using Python Continue reading on Towards Data Science » Ari Joury, PhD Go to original source
-
Synthetic Control Sample for Before and After A/B Test
Synthetic Control Sample for Before and After A/B Test Learn a simple way to use linear regression to create a synthetic control sample for your A/B test Continue reading on Towards Data Science » Gustavo R Santos Go to original source
-
Five Reasons You Cannot Afford Not Knowing Probability Proportional to Size (PPS) Sampling
Five Reasons You Cannot Afford Not Knowing Probability Proportional to Size (PPS) Sampling Data Science Simple Random Sampling (SRS) works, but if you do not know Probability Proportional to Size Sampling (PPS), you are risking yourself some critical statistical mistakes. Learn why, when, and how you can use PPS Sampling here! Photo by Justin Morgan on Unsplash…