Two‐step method

One of the commonly used approaches to estimate HTE is a two‐step method, 21 which builds separate regression models for the treatment and control groups. This counterfactual model consisting of the two constructed regression models is then used to estimate the counterfactual differences in individual outcomes to infer the individual treatment effects. Specifically, for an individual with distinct covariate values, each regression model can project outcome values, and the difference between the two outcomes will represent the predicted treatment effect. The two‐step method has been conventionally used in many fields, such as econometrics, 22 social science, 23 epidemiology, 24 and medical science. 25 Despite being intuitive and straightforward to implement, this method can be constrained by the nature of linear regression, which imposes linear relationships unless more complex relationships are explicitly predefined in the model. As such, its performance can be significantly compromised in the presence of model misspecification for complex relationships. Another intrinsic drawback is that the difference between the two independent “accurate” models does not necessarily lead to an accurate HTE estimate.