Exploring Causality in Healthcare Decisions

Aug 15, 2024

Lecture on Causality

Key Concepts

  • Causality: Understanding the underlying data generating process is crucial, especially for answering causal questions rather than just predictive ones.
  • Correlation vs. Causation: Commonly heard that "correlation does not imply causation."
  • Predictive vs. Causal Questions: Predictive models may not answer causal questions relevant in fields like healthcare.

Importance of Causality in Healthcare

  • Predictive models help in early detection, e.g., diabetes, but the ultimate aim is to prevent health issues.
  • Example: Gastric bypass surgery appearing as a negative weight in predictive models raises causal questions.
  • Diagnosis and treatment decisions are inherently causal rather than purely predictive.

Causal Inference

  • Potential Outcomes Framework: Focuses on counterfactuals (what would happen under different scenarios).
  • Causal Graphs: Visual representations showing causal relationships; important for causal inference.

Examples of Causal Questions

  • Does gastric bypass prevent diabetes?
  • How do treatment decisions influence patient outcomes?
  • Does smoking cause lung cancer? (Can't be tested ethically with randomized trials)

Methods and Assumptions

  • Propensity Scores: Used to estimate the probability of treatment to ensure overlap between treated and untreated groups.
  • No Unobserved Confounding: Assumes all relevant factors affecting treatment and outcome are observed.
  • Common Support (Overlap): Ensures some probability for each treatment across different subpopulations.

Adjustment Formula

  • Adjustment Formula: Used to estimate causal effects from observational data.
  • Requires assumptions of no unobserved confounding and common support.
  • Covariate Adjustment: Learning a function to predict outcomes based on both covariates and treatment.

Challenges and Considerations

  • High dimensional data complicates traditional statistical approaches.
  • Machine learning reduction must focus on inputs, interventions, and outcomes.
  • Potential biases need to be addressed, especially in high-dimensional settings.

QA and Discussion

  • Concerns about the quality of function learning in machine learning models for causal inference.
  • Importance of having valid assumptions and functional forms for accurate causal inferences.

Conclusion

  • Emphasis on understanding causal relationships in data for making informed decisions, particularly in healthcare.
  • Recognizing the limitations of predictive models and the importance of incorporating causal inference methods.