Causal inference methods need validation, and validation needs ground truth. In observational data, ground truth doesn't exist — you never observe the counterfactual. Synthetic data provides it: you define the causal mechanism, generate the data, then test whether the estimator recovers what you specified. But synthetic data faces a dilemma. Realistic distributions lack precise causal control. Precisely controlled distributions lack realism. The data is either a good test or a good simulation, rarely both.
Taha et al. build CausalMix to decouple these dimensions. A mixture-of-Gaussians latent model captures realistic distributional structure. Specialized decoders handle different variable types. Three independent knobs control causal properties: propensity-score overlap (how similar treated and untreated groups are), confounding strength (how much shared causes influence both treatment and outcome), and treatment effect heterogeneity (how much the effect varies across individuals).
The independence of the knobs is the contribution. Standard simulators entangle these properties — increasing confounding changes the overlap, which changes the effective heterogeneity. CausalMix allows researchers to vary one while holding the others fixed. This turns estimator evaluation from a single benchmark score into a three-dimensional stress test: which methods fail under weak overlap? Which are robust to strong confounding but fragile to heterogeneity?
The through-claim is about the dimensionality of validation. A single benchmark score (RMSE on one synthetic dataset) is a point estimate in a three-dimensional failure space. Methods that look equivalent at one point can diverge wildly at another. The sandbox doesn't just test estimators — it maps the topology of their failure modes.
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