When A/B Tests Can’t Be Done

Module Instructor

Banani Mohapatra

Senior Manager, AI/ML & Data Science at Walmart

Walmart

In this module, you'll learn new methods or alternatives when A/B testing cannot be done. These are very common concepts for Data Scientists and Product Analysts asked during interviews.

Module Overview

Related Roles

Product Data Scientist

Data Analyst

Module Tags

Experimentation

A/B Test

Propensity Score

PSM

Causal Inference

Related Roles

Product Data Scientist

Data Analyst

Module Tags

Experimentation

A/B Test

Propensity Score

PSM

Causal Inference

Time Estimate

Syllabus

1 chapter7 sections

1. Introduction to Casual Inference

Introduction to Casual Inference

2. Casual Inference Methods

Time Estimate

45 min

Difficulty

Advanced

45 min

Difficulty

Advanced

Casual Inference Methods

3. Propensity Score Matching (PSM) Introduction

Propensity Score Matching (PSM) Introduction

4. Propensity Score Matching (PSM) Core Idea

Propensity Score Matching (PSM) Core Idea

5. Propensity Score Matching (PSM) Methodology

Propensity Score Matching (PSM) Methodology

6. Propensity Score Matching (PSM) Design

Propensity Score Matching (PSM) Design

7. Propensity Score Marching (PSM) Real World Applications

Propensity Score Marching (PSM) Real World Applications

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Modules

When A/B Tests Can’t Be Done

Syllabus

Content

Modules

When A/B Tests Can’t Be Done

Syllabus

ContentIn this chapter, you'll learn new methods or alternatives when A/B testing cannot be done.7 sectionsView Chapter

Content