Steamforged Sorcery: The Complete Series: A LitRPG Boxset

This book is an introduction to Causal Inference in Python, but it is not an introductory book in general. It’s introductory because I’ll focus on application, rather than rigorous proofs and theorems of causal inference; additionally, when forced to choose, I’ll opt for a simpler and intuitive explanation, rather than a complete and complex one. It is not introductory in general because I’ll assume some prior knowledge about Machine Learning (ML), statistics and programming in Python. It is not too advanced either, but I will be throwing in some terms that you should know beforehand. How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example. Here is a non exhaustive list of the things I recommend you know before reading this book: - Basic knowledge of Python, including the most commonly used data scientists libraries: Pandas, Numpy, Matplotlib, Scikit-Learn. I come from an Economics background, so you don’t have to worry about me using very fancy code. Just make sure you know the basics pretty well. - Knowledge of basic statistical concepts like distributions, probability, hypothesis testing, regression, noise, expected values, standard deviation, independence. I will include a statistical review in the book in case you need a refresher. - Knowledge of basic data science concepts, like machine learning model, cross validation, overfitting and some of the most used machine learning models (gradient boosting, decision trees, linear regression, logistic regression). With this book, you will: Learn how to use basic concepts of causal inference Frame a business problem as a causal inference problem Understand how bias gets in the way of causal inference Learn how causal effects can differ from person to person Use repeated observations of the same customers across time to adjust for biases Understand how causal effects differ across geographic locations Examine noncompliance bias and effect dilution The main audience of this book is Data Scientists who are working in the industry. If you fit this description, there is a pretty good chance that you cover the prerequisites that I’ve mentioned. Also, keep in mind that this is a broad audience, with very diverse skill sets. For this reason, I might include some note or paragraph which is meant for the most advanced reader. So don’t worry if you don’t understand every single line in this book. You’ll still be able to extract a lot from it. And maybe it will come back for a second read once you mastered some of its basics. Preface Prerequisites Outline 1. Introduction To Causal Inference What is Causal Inference Why we Do Causal Inference Machine Learning and Causal Inference Association and Causation The Treatment and the Outcome The Fundamental Problem of Causal Inference Causal Models Interventions Individual Treatment Effect Potential Outcomes Consistency and No Interference Assumptions Causal Quantities of Interest Causal Quantities: An Example Bias The Bias Equation A Visual Guide to Bias Identifying the Treatment Effect The Independence Assumption Identification with Randomization Chapter Key Ideas Other Examples A Glass of Wine a Day Keeps the Doctor Away An Incredible Membership Program 2. Randomized Experiments and Stats Review Brute Force Independence with Randomization An A/B Testing Example Checking for Balance The Ideal Experiment The Most Dangerous Equation The Standard Error of Our Estimates Confidence Intervals Hypothesis Testing Null Hypothesis Test Statistic P-values Power Sample Size Calculation Key Ideas Other Examples The Effectiveness of COVID19 Vaccines Face-to-Face vs Online Learning. 3. Graphical Causal Models Thinking About Causality Visualizing Causal Relationships Are Consultants Worth it? Crash Course in Graphical Models Chains Forks Immorality or Collider The Flow of Association Cheat Sheet Querying a Graph in Python Identification Revisited CIA and The Adjustment Formula Positivity Assumption An Identification Example with Data Confounding Bias Randomization Revisited Selection Bias Conditioning on a Collider Adjusting for Selection Bias Conditioning on a Mediator Key Ideas Other Examples Conditioning on the Positives The Hidden Bias in Survival Analysis 4. The Unreasonable Effectiveness of Linear Regression All You Need is Linear Regression Why We Need Models Regression in A/B Tests Adjusting with Regression Regression Theory Single Variable Linear Regression Multivariate Linear Regression Frisch-Waugh-Lovell Theorem and Orthogonalization Debiasing Step Denoising Step Standard Error of the Regression Estimator Final Outcome Model FWL Summary Regression as an Outcome Model Positivity and Extrapolation Non-Linearities in Linear Regression Linearizing the Treatment Non-Linear FWL and Debiasing Regression for Dummies Conditionally Random Experiments Dummy Variables Saturated Regression Model Regression as Variance Weighted Average De-Meaning and Fixed Effects Omitted Variable Bias: Confounding Through the Lens of Regression Neutral Controls Noise Inducing Control Feature Selection: A Bias-Variance Trade-Off Key Ideas Other Examples Public or Private Schools? Marketing Mix Modeling 5. Propensity Score The Impact of Management Training Adjusting with Regression Propensity Score Propensity Score Estimation Propensity Score and Orthogonalization Inverse Propensity Weighting Variance of IPW Stabilized Propensity Weights Pseudo-Populations Selection Bias Bias-Variance Trade-Off Positivity Doubly Robust Estimation Treatment is Easy to Model Outcome is Easy to Model Generalized Propensity Score for Continuous Treatment Keys Ideas Other Examples Causal Contextual Bandits