morpateFit

Purpose

Estimates average treatment effects (ATE) using a multivariate ordered response probit (MORP) model by systematically modifying selected covariates and computing the resulting predicted probabilities.

Format

out = morpATEFit(fname, dvordname, davordname, ivord, changevar, changeval[, ctl])
Parameters:
  • fname (string) – Name of the dataset file to load.
  • dvordname (Kx1 string vector) – Vector of dependent ordinal variable names.
  • davordname (Kx1 string vector) – Vector of alternative availability variable names.
  • ivord (KxM string matrix) – Matrix of independent variable names for the ordered response.
  • changevar (Px1 string vector) – Vector of variable names to modify for counterfactual evaluation.
  • changeval (Px1 vector) – Vector of values to assign to changevar for counterfactual evaluation.
  • ctl (struct) –

    Optional. Instance of a morpControl structure for advanced control of estimation options. If not provided, defaults are used.

    Member Type Default Description
    ctl.method string "OVUS" Analytic approximation method to use in estimation.
    ctl.spher scalar 0 If 1, uses spherical parameterization; if 0, uses radial parameterization.
    ctl.indep scalar 0 If 1, assumes independence across equations; if 0, allows correlation.
    ctl.indepfirst scalar 0 If 1, estimates the independence model first before correlated estimation.
    ctl.correst matrix {} Correlation restriction matrix for advanced restriction specifications.
Returns:

out (Cx(L+1) matrix, where C = total combinations evaluated, L = number of ordinal variables) – Matrix containing each evaluated combination of ordinal levels and its corresponding predicted mean probability.

Details

  • Loads the dataset, modifies changevar columns to changeval for counterfactual estimation.
  • Evaluates all possible combinations of ordinal outcome levels systematically.
  • Computes predicted probabilities for each combination using the fitted MORP model (lpr1_morp).
  • Outputs a matrix combining the evaluated level combinations with their average predicted probabilities.
  • Reports average predicted probabilities for specific target levels (e.g., level 3) for key outcomes such as happiness, meaningfulness, stress, and tiredness.

Library

bhatlib

Source

morpfit.src

See also

morpfit()