Mis on heterogeenne ravi mõju ja kuidas seda otsida?

Heterogeneous Treatment Effect (HTE) is the effect of an intervention (e.g., treatment, advertising campaign, interface change) that varies depending on the characteristics of individual subgroups or even individual users. Unlike the Average Treatment Effect (ATE), which shows the average effect across the entire population, HTE allows understanding which segments experience stronger or weaker effects.

To find HTE, the following approaches are commonly used:

1. Stratification — dividing data into subgroups based on features (age, region, behavior) and evaluating the effect within each group.
2. Interaction models — building regression models with interactions between the treatment feature and other variables.
3. Machine learning methods — using algorithms specifically designed for HTE estimation, such as Causal Forest, uplift models, targeted decision trees.

Example using Causal Forest (in Python with the econml library):

from econml.grf import CausalForest

# X - features, T - binary treatment, y - outcome
model = CausalForest()
model.fit(y, T, X)

# Predicting treatment effect for each object
hte = model.effect(X)

Thus, HTE helps make more accurate decisions by tailoring interventions to different user segments.