Causal Inference
A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make y...
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Cambridge
MIT Press
2023
The MIT Press |
| Edition | 1 |
| Series | The MIT Press Essential Knowledge Series |
| Subjects | |
| Online Access | Get full text |
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| Abstract | A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce? Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy. |
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| AbstractList | Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce? Causal Inference provides a brief and nontechnical introduction to randomised experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy. Series Overview: The MIT Press Essential Knowledge series offers accessible, concise, beautifully produced books on topics of current interest. Written by leading thinkers, the books in this series deliver expert overviews of subjects that range from the cultural and the historical to the scientific and the technical. In today's era of instant information gratification, we have ready access to opinions, rationalizations, and superficial descriptions. Much harder to come by is the foundational knowledge that informs a principled understanding of the world. Essential Knowledge books fill that need. Synthesizing specialized subject matter for nonspecialists and engaging critical topics through fundamentals, each of these compact volumes offers readers a point of access to complex ideas. A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce? Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy. |
| Author | Rosenbaum, Paul R |
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| Copyright | 2023 Massachusetts Institute of Technology |
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| Keywords | randomized experiment sensitivity analysis Causation causality observational study epidemiology health outcomes randomized trials treatment effects econometrics propensity data science randomization program evaluation medical statistics policy analysis clinical trials public health statistics |
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| Notes | Includes bibliographical references (p. [189]-195) and index |
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| Snippet | A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral... Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the... |
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| SubjectTerms | Causation Inference Mathematical statistics Observation (Scientific method) Probabilities Science Science -- Experiments |
| TableOfContents | Intro -- Contents -- Series Foreword -- List of Examples -- List of Methodological Topics -- 1 The Effects Caused by Treatments -- 2 Randomized Experiments -- 3 Observational Studies: The Problem -- 4 Adjustments for Measured Covariates -- 5 Sensitivity to Unmeasured Covariates -- 6 Quasi-Experimental Devices in the Design of Observational Studies -- 7 Natural Experiments, Discontinuities, and Instruments -- 8 Replication, Resolution, and Evidence Factors -- 9 Uncertainty and Complexity in Causal Inference -- Postscript: Key Ideas, Chapter by Chapter -- Glossary -- Notes -- Bibliography -- Further Reading -- Index 7: Natural Experiments, Discontinuities, and Instruments -- Bits and Pieces of Random Assignment in an Otherwise Biased World -- Natural Experiments from Lotteries -- Nature's Natural Experiment, I: The Genes of Siblings -- Nature's Natural Experiment, II: Hypothetical Siblings -- Discontinuity Designs as Natural Experiments -- Encouragement Experiments: Can You Learn about One Treatment When You Randomize Another? -- Instrumental Variables and the Complier Average Causal Effect -- The Effect of Being Offered a Housing Voucher or the Effect of Accepting It -- Choosing Situations in Which Biases in Treatment Assignment Are Smaller -- 8: Replication, Resolution, and Evidence Factors -- Replication Is Not Repetition -- Repetition without Resolution -- Varied Views of a Single Object -- Evidence Factors -- 9: Uncertainty and Complexity in Causal Inference -- Are Small Daily Doses of Alcohol Beneficial? -- Oncologists versus Cardiologists -- A Dissenting Voice from a New Tactic: Mendelian Randomization -- The Answer Might Be Complex -- A Traditional Toxin -- Total Mortality -- Is Part or All of the Supposed Heart Benefit Simply a Mistake? -- So Are Small Daily Doses of Alcohol Beneficial? -- Postscript -- Glossary -- Notes -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- Chapter 9 -- Bibliography -- Further Reading -- Index Intro -- Contents -- Series Foreword -- List of Examples -- List of Methodological Topics -- 1: The Effects Caused by Treatments -- What Is a Causal Effect? Why Is Causal Inference Difficult? -- Notation for a Precise Question -- Would a Control Group Solve the Problem? -- It Is Just the Same with More Than Two People -- Flipping a Fair Coin to Assign Treatments to People -- The Average Treatment Effect (ATE) -- 2: Randomized Experiments -- The Effects of Treatments for Ebola Virus Disease -- A Randomized Controlled Trial -- Why Assign Treatments at Random? -- Randomized Treatment Assignment and Causal Inference -- Estimating the Average Treatment Effect -- Testing the Hypothesis of No Effect -- What Is Special about Coin Flips? -- Bleeding George Washington and the Theory of Humors -- 3: Observational Studies -- What Are Observational Studies? -- Smoking and Periodontal Disease -- Interlude: Tukey's Boxplot -- Do Treated and Control Groups Look Comparable in Boxplots? -- Comparing Outcomes in Groups That Are Not Comparable -- 4: Adjustments for Measured Covariates -- Matching for Covariates as a Method of Adjustment -- Imbalances in the Propensity Score -- How Matching for the Propensity Score Balances Measured Covariates -- Comparing Outcomes While Controlling Measured Covariates -- Other Matching Techniques -- 5: Sensitivity to Unmeasured Covariates -- Objections, Counterclaims, and Rival Hypotheses -- Smoking and Lung Cancer -- The First Sensitivity Analysis in an Observational Study -- Modern Sensitivity Analysis: Smoking and Periodontal Disease -- The Role of Sensitivity Analysis -- 6: Quasi-Experimental Devices in the Design of Observational Studies -- Anticipated Counterclaims -- Two Control Groups -- The Logic of Two Control Groups -- Untreated Counterparts in Addition to Untreated Controls -- Resolving Anticipated Counterclaims |
| Title | Causal Inference |
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