Causal Inference Opening Workshop - Difference-in-differences: more than meets the eye - Laura Hatfield, December 11, 2019
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This document discusses difference-in-differences (DiD) analysis, a quasi-experimental method used to estimate treatment effects. The author notes that while widely applicable, DiD relies on strong assumptions about the counterfactual. She recommends approaches like matching on observed variables between similar populations, thoughtfully specifying regression models to adjust for confounding factors, testing for parallel pre-treatment trends under different assumptions, and considering more complex models that allow for different types of changes over time. The overall message is that DiD requires careful consideration and testing of its underlying assumptions to draw valid causal conclusions.
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Causal Inference Opening Workshop - Difference-in-differences: more than meets the eye - Laura Hatfield, December 11, 2019
- 1. Difference-in-differences: more than meets the eye Laura A. Hatfield, PhD Associate Professor Department of Health Care Policy Harvard Medical School December 11, 2019
- 3. Health care produces lots of data Electronic health records Billing claims
- 4. This invites “quasi-experimental” analyses
- 5. A methodologist’s view on diff-in-diff Widely applicable Strong causal assumptions Our research Matching Regression Testing assumptions Related designs
- 6. Did hospital global budgets work?
- 8. “ ” The control group’s untreated outcome change stands in for the treated group’s untreated outcome change. Observed Counterfactual
- 9. The counterfactual assumption, visually Often (misleadingly) called “the parallel trends assumption” Maryland Virginia Pre Post Spending without global budgets
- 10. From counterfactual to treatment effect Maryland Virginia Treatment effect
- 11. Ideas for making counterfactuals more plausible Match on observed variables Test for parallel trends Adjust using regression Fit more complicated models
- 12. Match on observed variables “Beware regression to the mean.” Jamie Daw
- 13. Match on observed variables
- 14. Match on observed variables Matching from different populations creates bias Matching from same population ameliorates bias
- 15. “Specify regression models thoughtfully.” Adjust using regression models Bret Zeldow
- 16. Time-varying Independent of treatment Related to treatment Time-invariant Time-varying 𝑋𝑡 𝑋𝑡*time Time-invariant Time-varying 𝑋*time None Time-invariant Time-invariant Time-varying None 𝑋*time Covariate 𝑋 Effect of 𝑋 on 𝑌0 Adjustment
- 17. Adjust using regression models Align regression model with confounding
- 18. Test for parallel trends “Don’t use conventional null hypothesis tests.” Alyssa Bilinski
- 19. Test for parallel trends Examine changes under alternative assumptions
- 20. Fit more complicated models “Consider alternative causal assumptions.” Carrie Fry
- 21. Fit more complicated models Level change Intercept change Slope change Diff-in-Diff Comparative Interrupted Time Series
- 22. Fit more complicated models Modeling changes imply new causal assumptions
- 23. Matching from different populations creates bias Align regression model with confounding relationship Examine changes under alternative assumptions Modeling changes imply new causal assumptions @laura_tasticdiff.HealthPolicyDataScience.org
Hinweis der Redaktion
- Providers are experimenting with new ways of delivering care, such as telemedicine and hospital at home. Payers are experimenting with new ways of paying for care, such as price transparency and accountable care organizations.
- The health care delivery and payment systems throw off a huge quantity of “data exhaust” in the ordinary course of business.
- In 2016, Maryland put all the hospitals in the state under a new hospital global budget program