Create a flyer for "Data Inference in Observational Settings"

While causal thinking is at the heart of social science research and explanation, too little rigorous attention is paid by researchers as how to strengthen claims of causality.  This comprehensive collection draws together some of the best papers that point to the challenges of establishing causality and provide ways of addressing many of these challenges. It provides the resources to help both researchers and students address the question of causality much more systematically and convincingly than is often the case.

An excellent collection of seminal papers summarizing the background to, and the state of the art for, methods which are becoming central to the conduct of epidemiology and other areas of health and social research in the 21st century.

These are the canonical papers on causal inference, organized for the first time into one useful handbook. It’s a must-have for all researchers in the social sciences. I shall be recommending it to all my students.

These volumes bring together a core set of important papers on the critical topic of causal inference and will prove to be an extremely useful source for recommended core reading for researchers and students alike. 

This four-volume reader is the best place to start if you are interested in an overview of how to make cause inference from observational data. The selection concisely covers a vast literature that has rapidly developed over a period of several decades. You will read seminal methodological contributions, excellent review articles and important applications in these volumes. Instructors in the social sciences may use this reader for a graduate level methodology course. Researchers will find it a useful reference on their bookshelves.

For Chinese researchers and students, I believe a comprehensive collection of rigorous papers on causality will enhance the claims of study findings for a rapidly changing society. The handbook will provide a useful tool for researchers and students to meet the challenges of addressing causal relationships.

In social science research, oftentimes, the researcher’s ultimate goal is to be able to make causal inference statements about what would contribute to socially significant outcomes. Unfortunately, not being able to implement true experimental design in most social science research situations makes such causal inference risky and full of pitfalls, as it can become very difficult to rule out rival hypotheses or explanations.