Causal Inference: What If

2020

Miquel Hernan and Jamie Robins

The book is divided in three parts of increasing difficulty:

Part I is about causal inference without models (i.e., nonparametric identification of causal effects),

Part II is about causal inference with models (i.e., estimation of causal effects with parametric models).

Part III is about causal inference from complex longitudinal data (i.e., estimation of causal effects of time-varying treatments).

Throughout the text, we have interspersed Fine Points and Technical points that elaborate on certain topics mentioned in the main text. Fine Points are designed to be accessible to all readers while Technical Points are designed for readers with intermediate training in statistics. The book provides a cohesive presentation of concepts and methods for causal inference that are currently scattered across journals in several disciplines. We expect that it will be of interest to all professionals that make causal inferences, including epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists...

Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data.

Causal Inference is an admittedly pretentious title for a book. A complex scientific task, causal inference relies on triangulating evidence from multiple sources and on the application of a variety of methodological approaches. No book can possibly provide a comprehensive description of all methodologies for causal inference across the sciences. The authors of any Causal Inference book will have to choose which aspects of causal inference methodology they want to emphasize.

The title of this introduction reflects our own choices: a book that helps scientists–especially health and social scientists–generate and analyze data to make causal inferences that are explicit about both the causal question and the assumptions underlying the data analysis. Unfortunately, the scientific literature is plagued by studies in which the causal question is not explicitly stated and the investigators’ unverifiable assumptions are not declared.

This casual attitude towards causal inference has led to a great deal of confusion. For example, it is not uncommon to find studies in which the effect estimates are hard to interpret because the data analysis methods cannot appropriately answer the causal question (were it explicitly stated) under the investigators’ assumptions (were they declared). In this book, we stress the need to take the causal question seriously enough to articulate it, and to delineate the separate roles of data and assumptions for causal inference.

Once these foundations are in place, causal inferences become necessarily less casual, which helps prevent confusion. The book describes various data analysis approaches to estimate the causal effect of interest under a particular set of assumptions when data are collected on each individual in a population. A key message of the book is that causal inference cannot be reduced to a collection of recipes for data analysis.