Why Causal Inference Matters More Than Prediction in Development Research
March 15, 2026
Predictive accuracy is a seductive metric. A model that predicts child stunting with 90% AUC feels like progress. But in development research, prediction is rarely the goal. Intervention is.
The Core Problem#
When a ministry of health asks “which children should we target with nutritional support?”, they are not asking for a prediction model. They are asking a causal question: which children will benefit most from the intervention?
These are different questions. A model trained to predict outcomes from observational data conflates correlation with causal effect. High socioeconomic status correlates with good outcomes, but targeting rich children for nutrition programs would be absurd.
Potential Outcomes Framework#
The counterfactual framework makes this precise. For each child $i$, define:
- $Y_i(1)$: outcome if treated
- $Y_i(0)$: outcome if untreated
- $\tau_i = Y_i(1) - Y_i(0)$: individual treatment effect
We can never observe both $Y_i(1)$ and $Y_i(0)$ for the same child. This is the fundamental problem of causal inference. But we can estimate the average treatment effect $\mathbb{E}[\tau_i]$ or the conditional average treatment effect $\mathbb{E}[\tau_i \mid X_i = x]$ under assumptions.
What This Means for Our Work#
In the Green-NAS paper, we were careful to frame our contributions as predictive; we are not claiming that wider transformers cause better performance in all settings. In the child development encoder work, we explicitly model the deployment setting as a transfer learning problem, not a causal one.
The field needs more researchers who can draw this line clearly.