Why the problem isn't the model or the GPUs
Discussions on causal inference often revolve around algorithms and data volume. The uncomfortable truth is that the weakest link is conceptual: selection bias, missing counterfactuals, and misaligned assumptions. More data does not fix flawed identification.
When better data isn't enough
Teams believe that larger datasets or more sophisticated estimators automatically yield causal answers. In practice, missing counterfactuals and selection mechanisms break this hope. If the observed population systematically differs from the one you want to act upon, estimates are biased. Ignoring how data was generated leads to incorrect yet confident recommendations.
Understanding beats quantity
Causal inference requires explicit assumptions: which variables are confounders, how were units selected, what intervention are we simulating? Identification is a modeling phase: without it, estimation is simply curve-fitting. This means investing more time in study design, eliciting domain knowledge, and sensitivity analysis.
Checklist for robust causal claims
Start with a clear intervention and a defined target population, map the selection process, list possible unobserved confounders, and perform transparent sensitivity analyses. Use causal diagrams to document assumptions. When randomization isn't possible, combine design (instrumental variables, RDD) with domain constraints and external validation.
How the field will evolve
The future sees tools that integrate domain knowledge, automated sensitivity reports, and interactive workflows to encode assumptions. Next-gen innovations will highlight where assumptions fail, not just provide better estimators.
Focus on understanding
For causal answers, invest in understanding: the data generation story, selection mechanisms, and the limits of your claims. Conceptual clarity provides more value than another terabyte of logs.
Sources
- The Book of Why by Judea Pearl & Dana Mackenzie (2018)
- Causal Inference: The Mixtape by Scott Cunningham (2021)