R: Policy Learning Under Constraint

# Policy Learning Under Constraint

## Documentation for package ‘PLUCR’ version 0.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

binary_S_p | Constraint function for binary policy
check_data | Check input data for validity
CVFolds | CVFolds (from SuperLearner package)
delta_mu_constant | Constant Conditional Average Treatment Effect estimator for Y
delta_mu_linear | Linear-shaped Conditional Average Treatment Effect estimator for Y
delta_mu_mix | Mixed-shape Conditional Average Treatment Effect estimator for Y
delta_mu_null | Null Conditional Average Treatment Effect estimator for Y
delta_mu_realistic | Realistic Conditional Average Treatment Effect estimator for Y
delta_mu_threshold | Thresholded-shaped Conditional Average Treatment Effect estimator for Y
delta_nu_linear | Linear-shaped Conditional Average Treatment Effect estimator for Xi
delta_nu_mix | Mixed-shaped Conditional Average Treatment Effect estimator for Xi
delta_nu_realistic | Realistic Conditional Average Treatment Effect estimator for Xi
delta_nu_satisfied | Computes the difference in expected outcomes under treatment and control.
delta_nu_threshold | Thresholded Conditional Average Treatment Effect estimator for Xi
estimate_mu | Estimate mu
estimate_nu | Estimate nu
estimate_ps | Estimate propensity score
estimate_real_valued_mu | Estimate real-valued mu
FW | Frank-Wolfe algorithm
generate_data | Synthetic data generator and functions generator
generate_realistic_data | Realistic synthetic data generator and functions generator
get_opt_beta_lambda | Select Optimal Beta and Lambda Combination
grad_Lagrangian_p | Gradient of the objective function
grad_Lagrangian_p_X | Gradient of the objective function
HX | Compute the Inverse Propensity Score Weight (IPW)
Lagrangian_p | Objective function taking the form of a Lagrangian
learn_threshold | Learn Optimal Decision Threshold
lwr_upper_bound_estimators | Lower and upper bound estimators for policy value and constraint
main_algorithm | Main algorithm
make_psi | Generate psi function
model_Xi_linear | Linear treatment effect on Xi Component Function
model_Xi_mix | Mixed treatment effect on Xi component function
model_Xi_realistic | Realistic treatment effect on Xi Component Function
model_Xi_satisfied | Low treatment effect on Xi
model_Xi_threshold | Thresholded treatment effect on Xi component function
model_Y_constant | Constant treatment effect on Y component function
model_Y_linear | Linear treatment effect on Y component function
model_Y_mix | Mixed treatment effect on Y component function
model_Y_null | No treatment effect on Y component function
model_Y_realistic | Realistic treatment effect on Y component function
model_Y_threshold | Thresholded treatment effect on Y component function
naive_approach_algorithm | Naive approach main algorithm
Optimization_Estimation | Iterative optimization procedure
oracular_approach_algorithm | Oracular approach main algorithm
oracular_process_results | Oracular evaluation of a policy
phi | Normalize a Matrix by Column Min-Max Scaling
phi_inv | Inverse Min-Max Normalization
plot_metric_comparison | Plot metric values for comparison
plot_realistic | Plot realistic data setting
predict.SL.grf | predict.SL.grf
process_results | Evaluate a policy
R_p | Risk function for Conditional Average Treatment Effect (CATE)
SGD | Stochastic Gradient Descent (SGD) algorithm
sigma_beta | Link function
sigma_beta_prime | Derivative of link function
SL.grf | SL.grf
SuperLearner.CV.control | SuperLearner.CV.control (from SuperLearner package)
synthetic_data_plot | Plot synthetic data setting
S_p | Constraint function
update_mu | Update mu via augmented covariate adjustment
update_mu_XA | Update mu via augmented covariate adjustment for fixed X
update_nu | Update nu via augmented covariate adjustment
update_nu_XA | Update nu via augmented covariate adjustment for fixed X
visual_treatment_plot | Visualize treatment assignment probability
V_p | Oracular approximation of value function
V_Pn | Estimation of policy value