Precision in estimation of heterogeneous effects (PEHE)

Source: R/metrics.R

The root mean squared error between true and predicted conditional average treatment effects: $$PEHE = \sqrt{\frac{1}{n} \sum_i (\tau(x^{(i)}) - \hat\tau(x^{(i)}))^2}$$ Requires the true CATEs, so it is only usable with (semi-)synthetic benchmark data such as synth_test or ihdp_test.

pehe(tau_true, tau_pred)

Arguments

tau_true

Numeric vector of true CATEs.

tau_pred

Numeric vector of predicted CATEs (same length).

Value

A single non-negative number.

References

Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217-240.

See also

eps_ate(), r_loss()

Examples

pehe(c(1, 2, 3), c(1.1, 1.8, 3.2))
#> [1] 0.1732051