Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus in the Department of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He is the author of several books, including Observational Studies (1st edition 1995, 2nd edition 2002), Design of Observational Studies (1st edition 2010, 2nd edition 2020), Observation and Experiment: An Introduction to Causal Inference (2017), and Replication and Evidence Factors in Observational Studies (2021).  In joint work, he and Donald B. Rubin invented the propensity score in 1983.  For contributions to causal inference, he received from the Committee of Presidents of Statistical Societies the R. A. Fisher Award in 2019 and the George W. Snedecor Award in 2003.

• Preface.- Part I First Steps.- 1. Examples of Observational Studies.- 2. Causal Inference in Randomized Experiments.- 3. Some Background Topics in Statistics.- Part II Adjustments for Observed Covariates.- 4. Propensity Scores and Ignorable Treatment Assignment.- 5. Algorithms for Matching.- 6. Evaluating the Balance of Observed Covariates.- 7. Covariance Adjustment.- Part III Sensitivity of Inferences to Covariates That Were Not Observed.- 8. Sensitivity of Causal Inferences to Unmeasured Biases in Treatment Assignment.- 9. Design Sensitivity and the Choice of Statistical Methods.- 10. Study Design and Design Sensitivity.- 11. Efficiency of Sensitivity Analyses.- Part IV Quasi-experimental Devices.- 12. Known Effects in Observational Studies.- 13. Evidence Factors for Two Control Groups.- 14. Tightened Blocks for Complementary Analyses.- 15. A Look Back Along the Path Taken.- Some Books and Articles About Causal Inference.- Notation.- Solutions to Selected Problems.- Some Comments for Instructors.- Index.

Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus in the Department of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He is the author of several books, including Observational Studies (1st edition 1995, 2nd edition 2002), Design of Observational Studies (1st edition 2010, 2nd edition 2020), Observation and Experiment: An Introduction to Causal Inference (2017), and Replication and Evidence Factors in Observational Studies (2021).  In joint work, he and Donald B. Rubin invented the propensity score in 1983.  For contributions to causal inference, he received from the Committee of Presidents of Statistical Societies the R. A. Fisher Award in 2019 and the George W. Snedecor Award in 2003.

• Preface.- Part I First Steps.- 1. Examples of Observational Studies.- 2. Causal Inference in Randomized Experiments.- 3. Some Background Topics in Statistics.- Part II Adjustments for Observed Covariates.- 4. Propensity Scores and Ignorable Treatment Assignment.- 5. Algorithms for Matching.- 6. Evaluating the Balance of Observed Covariates.- 7. Covariance Adjustment.- Part III Sensitivity of Inferences to Covariates That Were Not Observed.- 8. Sensitivity of Causal Inferences to Unmeasured Biases in Treatment Assignment.- 9. Design Sensitivity and the Choice of Statistical Methods.- 10. Study Design and Design Sensitivity.- 11. Efficiency of Sensitivity Analyses.- Part IV Quasi-experimental Devices.- 12. Known Effects in Observational Studies.- 13. Evidence Factors for Two Control Groups.- 14. Tightened Blocks for Complementary Analyses.- 15. A Look Back Along the Path Taken.- Some Books and Articles About Causal Inference.- Notation.- Solutions to Selected Problems.- Some Comments for Instructors.- Index.

Paul R. Rosenbaum is the Robert G. Putzel Professor Emeritus in the Department of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He is the author of several books, including Observational Studies (1st edition 1995, 2nd edition 2002), Design of Observational Studies (1st edition 2010, 2nd edition 2020), Observation and Experiment: An Introduction to Causal Inference (2017), and Replication and Evidence Factors in Observational Studies (2021).  In joint work, he and Donald B. Rubin invented the propensity score in 1983.  For contributions to causal inference, he received from the Committee of Presidents of Statistical Societies the R. A. Fisher Award in 2019 and the George W. Snedecor Award in 2003.

• Preface.- Part I First Steps.- 1. Examples of Observational Studies.- 2. Causal Inference in Randomized Experiments.- 3. Some Background Topics in Statistics.- Part II Adjustments for Observed Covariates.- 4. Propensity Scores and Ignorable Treatment Assignment.- 5. Algorithms for Matching.- 6. Evaluating the Balance of Observed Covariates.- 7. Covariance Adjustment.- Part III Sensitivity of Inferences to Covariates That Were Not Observed.- 8. Sensitivity of Causal Inferences to Unmeasured Biases in Treatment Assignment.- 9. Design Sensitivity and the Choice of Statistical Methods.- 10. Study Design and Design Sensitivity.- 11. Efficiency of Sensitivity Analyses.- Part IV Quasi-experimental Devices.- 12. Known Effects in Observational Studies.- 13. Evidence Factors for Two Control Groups.- 14. Tightened Blocks for Complementary Analyses.- 15. A Look Back Along the Path Taken.- Some Books and Articles About Causal Inference.- Notation.- Solutions to Selected Problems.- Some Comments for Instructors.- Index.