Testing Claude Code: Data Science Causal Inference Modeling Performance
Testing Claude’s Causal Inference Capabilities: Can a Non-Technical Stakeholder with Claude Replace a specialized Data Scientist?
I tested whether a head of marketing or product with raw panel data could accurately estimate causal effects using only Claude—no data science team required. The results revealed both impressive capabilities and critical failures that have real implications for AI-assisted analytics.
Claude successfully implemented difference-in-differences models with multiple specifications and appropriately tested for parallel trends. The methodology was sound on the surface.
But here’s where things broke down:
When the parallel trends test failed, Claude flagged the violation with warnings but continued presenting results anyway. For a non-technical stakeholder, those warnings might get ignored, leading to decisions based on biased estimates. That’s a dangerous failure mode.
Another issue emerged with heterogeneous treatment effects (not requested in the prompt). When analyzing region-specific impacts across five treated regions, Claude made a critical modeling error—it failed to drop other treated regions when estimating individual treatment effects. This contaminated the control group and biased every regional estimate.
The broader lesson: AI coding assistants can execute sophisticated causal inference techniques, but they don’t stop you from using invalid results. You still need enough statistical knowledge to recognize when parallel trends violations invalidate your model, or when treatment/control group construction is fundamentally flawed.
Finally, Claude failed to test other reasonable solutions such as synthetic control or do any analysis related to model selection and justification.
For data science interview prep, this matters. Companies running large-scale experiments expect you to understand not just how to run DiD models, but when the identifying assumptions fail and what that means for decision-making. You need to demonstrate judgment alongside technical execution.
The barrier to implementing causal inference has dropped dramatically. But the barrier to doing it correctly hasn’t changed—you still need to understand the methodology.
For consulting and coaching services, data science interview resources, and more, visit: https://www.whatstheimpact.com
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