Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Jul 29·5 min read
Why Your Best Predictive Model Gives the Wrong Treatment Effect
TL;DR
Predictive models often fail to account for confounding variables, leading to incorrect treatment effect estimates. Bayesian Adjustment for Confounding offers a solution to this issue.
✦ Why It Matters
Implement Bayesian Adjustment for Confounding in your predictive models to improve treatment effect accuracy today.
Key Takeaways
- Use exposure models to identify true confounders.
- Avoid relying solely on predictive metrics like BIC for model selection.
- Consider the causal relationships between variables in your analysis.
- Be cautious of over-adjusting for variables that may amplify bias.
How It Works
BAC connects two models: one for the outcome and one for the exposure. By ensuring that covariates selected for the outcome model are also considered in the exposure model, BAC helps identify true confounders that might be overlooked when focusing solely on predictive accuracy.