Leaf-averaging fixed-effects causal forests compress CATE heterogeneity by a slope below one when fitted on panel data, and the paper characterizes this attenuation and shows an out-of-bag linear rescaling recovers most of the lost spread.
Calibration of heterogeneous treatment effects in randomized experiments
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Attenuated Heterogeneity in Fixed-Effects Causal Forests, and a Cross-Fitted Correction
Leaf-averaging fixed-effects causal forests compress CATE heterogeneity by a slope below one when fitted on panel data, and the paper characterizes this attenuation and shows an out-of-bag linear rescaling recovers most of the lost spread.