Full Tutorial: Causal Inference and A/B Testing for Data Scientists in R (Feat. Tidymodels)

Hey future Business Scientists, welcome back to my Business Science channel. This is Learning Lab 89 where I shared how I do Causal Inference for Data Scientists in R. This FULL TUTORIAL is JAMMED packed with value (literally 6 weeks of research went into it). I cover an in-depth R Causal Inference workshop that covers a Hotel Business Case, 3 Strategies to improve operations with Causal Inference, Tidymodels, Google Causal Impact, Facebook Meta's GeoLift, and more! These tutorials are broken into 3 parts that cover Beginner, Intermediate, and Advanced techniques.

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Table of Contents:
00:00 Causal Inference for Data Scientists in R (Feat. Tidymodels)
01:05 Agenda for the Causal Inference Workshop
02:45 My Background in R
05:27 Causal Inference Training Structure (Beginner, Intermediate, & Advanced)
07:50 Business Case Study: Hotels Bookings & Cancellations
09:58 PART 1: A/B Testing for Causal Inference (Randomized Control Experiment) (Beginner)
14:01 Libraries, Data, and Experiment Setup
20:00 Data Exploration of Pre-Test and Experiment Data
25:13 A/B Testing: Difference in Means with 2-Sided T-Test
30:25 Average Treatment Effect (ATE) and Return On Adspend (ROAS)
32:22 PART 2: Geo-Experiments with Facebook GeoLift and Google CausalImpact (Intermediate)
34:22 Google Causal Impact for Return on Adspend
37:09 Facebook GeoLift for Geo-Experiments
42:01 PART 3: Hotel Cancelations with Pre-Experiment Data & Tidymodels (Advanced)
45:25 Libraries, Data, & Cost Analysis
48:22 Data Processing & Feature Engineering
49:15 Correlation Analysis (Level 1: Causal Hierarchy Association)
55:16 Association Graph (Correlation Graph): Top 4 Features
1:00:05 Causal Hypothesis
1:02:49 Simple Logistic Regression Model w/ Tidymodels
1:05:25 Considering Confounders: Penalized Logistic Regression Model with Tidymodels
1:12:01 Bootstrap Confidence Intervals (CI)
1:14:40 How to Create a Good Experiment from the Machine Learning Model
1:15:49 Conclusions: How to make $150,000 per year with these skills

#DataScience #CausalInference #MachineLearning #Rstats