Book contents
- Frontmatter
- Contents
- Boxes
- Contributors
- Introduction and Overview
- 1 Overview of Performance Analytics for Healthcare with Examples in R
- 2 Cost-Effectiveness Analysis for Healthcare: From Theory to Practice to Problems and Solutions
- 3 Capabilities, QALYs, and COVID
- 4 The Economic Efficiency of Policies to Reduce Ill Health Involving Environmental Factors
- 5 Health in the National Accounts
- 6 Healthcare as Social Infrastructure: Productivity and the UK National Health Service During and After COVID-19
- 7 Health, Human Capital, and Its Contribution to Economic Growth
- 8 What Do We Know from the Vast Literature on Efficiency and Productivity in Healthcare? A Review and Bibliometric Analysis
- 9 Brief Overview of Production Theory for Analyzing Healthcare Performance
- 10 Modeling Production of Well-Being from an Intermediate Medical Intervention: With an Empirical Demonstration
- 11 Data Envelopment Analysis Applications and US Hospital Policy
- 12 New Tools for Evaluating the Performance of Healthcare Providers Using DEA and FDH Estimators
- 13 Stochastic Frontier Analysis for Healthcare, with Illustrations in R
- 14 A Review of US Stochastic Frontier Studies of Hospital Efficiency Published After 2008
- 15 A Nonparametric Journey through Conditional Frontier Models
- 16 Measuring Health and Healthcare Efficiency: Revised Guidelines for Measurement
- 17 A Brief Introduction to Causal Inference in Healthcare
- 18 Dynamic Assignment of Patients to Primary and Secondary Inpatient Units: Is Patience a Virtue?
- Index
17 - A Brief Introduction to Causal Inference in Healthcare
Published online by Cambridge University Press: 21 November 2024
- Frontmatter
- Contents
- Boxes
- Contributors
- Introduction and Overview
- 1 Overview of Performance Analytics for Healthcare with Examples in R
- 2 Cost-Effectiveness Analysis for Healthcare: From Theory to Practice to Problems and Solutions
- 3 Capabilities, QALYs, and COVID
- 4 The Economic Efficiency of Policies to Reduce Ill Health Involving Environmental Factors
- 5 Health in the National Accounts
- 6 Healthcare as Social Infrastructure: Productivity and the UK National Health Service During and After COVID-19
- 7 Health, Human Capital, and Its Contribution to Economic Growth
- 8 What Do We Know from the Vast Literature on Efficiency and Productivity in Healthcare? A Review and Bibliometric Analysis
- 9 Brief Overview of Production Theory for Analyzing Healthcare Performance
- 10 Modeling Production of Well-Being from an Intermediate Medical Intervention: With an Empirical Demonstration
- 11 Data Envelopment Analysis Applications and US Hospital Policy
- 12 New Tools for Evaluating the Performance of Healthcare Providers Using DEA and FDH Estimators
- 13 Stochastic Frontier Analysis for Healthcare, with Illustrations in R
- 14 A Review of US Stochastic Frontier Studies of Hospital Efficiency Published After 2008
- 15 A Nonparametric Journey through Conditional Frontier Models
- 16 Measuring Health and Healthcare Efficiency: Revised Guidelines for Measurement
- 17 A Brief Introduction to Causal Inference in Healthcare
- 18 Dynamic Assignment of Patients to Primary and Secondary Inpatient Units: Is Patience a Virtue?
- Index
Summary
This chapter focuses on causal inference in healthcare, emphasizing the need to identify causal relationships in data to answer important questions related to efficacy, mortality, productivity, and care delivery models. The authors discuss the limitations of randomized controlled trials due to ethical or pragmatic considerations and introduce quasi-experimental research designs as a scientifically coherent alternative. They divide these designs into two broad categories, independence-based designs and model-based designs, and explain the validity of assumptions necessary for each design. The chapter covers key concepts such as potential outcomes, selection bias, heterogeneous treatment effects bias, average treatment effect, average treatment effect for the treated and untreated, and local average treatment effect. Additionally, it discusses important quasi-experimental designs such as regression discontinuity, difference-in-differences, and synthetic controls. The chapter concludes by highlighting the importance of careful selection and application of these methods to estimate causal effects accurately and open the black box of healthcare.
Keywords
- Type
- Chapter
- Information
- The Cambridge Handbook of HealthcareProductivity, Efficiency, Effectiveness, pp. 553 - 611Publisher: Cambridge University PressPrint publication year: 2024