Experiments & Observational Studies:Causal Inference in StatisticsPaul R. RosenbaumDepartment of StatisticsUniversity of PennsylvaniaPhiladelphia, PA 19104-63401 My Concept of a ‘Tutorial’• In the computer era, we often receive compressedfiles, .zip. Minimize redundancy, minimize storage,at the expense of intelligibility.• Sometimes scientific journals seemed to have beencompressed.• Tutorial goal is: ‘uncompress’. Make it possible toread a current article or use current software withoutgoing back to dozens of earlier articles.2 A Causal Question• At age 45, Ms. Smith is diagnosed with stage IIbreast cancer.• Her oncologist discusses with her two possible treat-ments: (i) lumpectomy alone, or (ii) lumpectomyplus irradiation. They decide on (ii).• Ten years later, Ms. Smith is alive and the tumorhas not recurred.• Her surgeon, Steve, and her radiologist, Rachael de-bate.• Rachael says: “The irradiation prevented the recur-rence–without it, thetumorwouldhaverecurred.”• Steve says: “You can’t know that. It’s a fantasy –you’re making it up. We’ll never know.”3 Many Causal Questions• Steve and Rachael have this debate all the time.AboutMs. Jones,whohadlumpectomyalone. AboutMs. Davis, whose tumor recurred after a year.• Whenever a patient treated with irradiation remainsdisease free, Rachael says: “It was the irradiation.”Steve says: “You can’t know that. It’s a fantasy.We’ll never know.”• Rachael says: “Let’s keep score, add ’em up.” ...
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