Statistical Models and Causal Inference

This book offers a critical look at statistical modeling in social sciences, arguing for a grounded approach over complex techniques. Statistical Models and Causal Inference reshapes understanding of research design.

In Statistical Models and Causal Inference, David A. Freedman provides a comprehensive examination of the principles and challenges surrounding statistical modeling within the social sciences. He argues that many contemporary technical advancements in statistical methods may not represent genuine progress, but rather a regression in understanding. Freedman emphasizes the importance of recognizing the limitations of these new approaches and illustrates his points through relevant examples drawn from fields such as political science, public policy, law, and epidemiology.

Freedman advocates for a 'shoe leather' methodology, which prioritizes natural variation and a deep understanding of the subject matter over complex statistical techniques. This approach aims to reduce confounding factors and enhance the reliability of research findings. Despite initial skepticism regarding his preference for simpler methods, the growing consensus among social scientists supports his view that robust research design and subject matter expertise are crucial components of effective statistical analysis.

Statistical Models and Causal Inference serves as an integrated presentation of Freedman's insights, encouraging researchers to rethink their reliance on sophisticated statistical tools. By highlighting the necessity of meticulous research designs, Freedman contributes to a more nuanced understanding of causal inference in the social sciences. His work challenges the status quo and promotes a more grounded approach to statistical modeling, making it a significant contribution to the field.