The data analyst's guide to cause and effect : an introduction to causal inference in practice / Theiss Bendixen, Benjamin Grant Purzycki.

Format:

Book

Author/Creator:

Bendixen, Theiss, author.

Purzycki, Benjamin Grant, author.

Series:

Quantitative applications in the social sciences ; no. 07-201.

Quantitative applications in the social sciences ; 201

Language:

English

Subjects (All):

Causation.

Inference.

Quantitative research.

Physical Description:

xx, 145 pages : illustrations, charts ; 22 cm.

Other Title:

Introduction to causal inference in practice

Place of Publication:

Thousand Oaks, California : Sage, [2027]

Summary:

Understanding cause-and-effect relationships is essential for credible research and informed decision-making. The Data Analyst's Guide to Cause and Effect offers a clear, practical roadmap for answering causal questions using both experimental and observational data. Built around the EEESI workflow--Estimand, Estimator, Estimate, Simulation-based Inference--this book provides a systematic approach to defining, estimating, and validating causal effects. Readers will learn to apply modern techniques such as g-methods, inverse probability weighting, poststratification, and multilevel modeling, while tackling challenges like confounding and missing data. With hands-on examples in R, code snippets, and simulation exercises, this guide balances rigor with accessibility. Ideal for graduate courses and applied researchers, it equips readers to move beyond simple associations and make credible causal inferences that inform theory, policy, and practice. -- Publisher's website

Contents:

Introduction

Causal Graphs

G-methods and Marginal Effects

Adventures in G-methods

Most of Your Data is Almost Always Missing

More Missing Data

Multilevel modelling and Mundlak's legacy

Causal Inference is not Easy

Notes:

Includes bibliographical references (pages 136-141) and index.

ISBN:

9798348848712

OCLC:

1563990150