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. *** LIMITED TIME OFFER: FULL R-TRACK SYSTEM *** 💥 Learn R with Me: https://learn.business-science.io/r-track-career-bundle?el=youtube *** JOIN LEARNING LABS PRO *** 💥 GET THE DATA + CODE IN THIS VIDEO: https://university.business-science.io/p/learning-labs-pro?el=youtube 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