# Outcome Models

After the pseudo population dataset was generated, we apply outcome models on the pseudo population as-if the dataset is from a randomized experiment.

We propose three types of outcome models using parametric, semi-parametric and non-parametric approaches, respectively.

`estimate_pmetric_erf` estimates the hazard ratios using a parametric regression model. By default, call `gnm` library to implement generalized nonlinear models.

`estimate_semipmetric_erf` estimates the smoothed exposure-response function using a generalized additive model with splines. By default, call `gam` library to implement generalized additive models.

`estimate_npmetric_erf` estimates the smoothed exposure-response function using a kernel smoothing approach. By default, call `KernSmooth` library to implement local polynomial fitting with a kernel weight. We use a data-driven bandwidth selection.