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README.md

Treatment Policy Learning with Fully Observed Outcomes

Overview

In several medical settings, lab testing provides retrospective access to patient outcomes under a variety of different treatments, but this information is typically unavailable to clinicians when making a treatment decision. We can leverage this data to learn more effective treatment policies for use on future patients.

This library provides implementations of three approaches for learning treatment policies in the setting with fully observed outcomes and (potentially) multiple objectives:

  • Thresholding
  • Expected Reward Maximization
  • Direct Learning

For more information about these approaches and their various trade-offs, check out our paper: Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes (KDD 2020).

Requirements

This library is built on Python 3 with pandas, sklearn, PyTorch, and numpy. If using XGBoost for training conditional outcome models in the indirect approaches, installation of the xgboost package is also required.

TODO: add setup script for automatically creating conda env with all necessary packages for running this code

Usage

This library is intended to be agnostic to any particular setting, and thus requires user-specified definitions of a few functions before it can be applied to a new setting of interest. Before running an experiment, the user should define:

  • Policy Metrics : In utils/metrics.py, the users should provide their desired implementation of the get_metrics function, which computes quantities evaluating the quality of a provided policy.

    This function takes as input a Pandas DataFrame containing treatment outcomes for the dataset of interest, along with the chosen action for each example. It should return a dictionary mapping metric names to the computed values for the provided policy.

  • Reward Function: This is only necessary if using the expected reward maximization or direct learning approaches, which both rely on a notion of a reward function for learning optimal policies.

    In utils/rewards.py, users should implement their desired version of the get_reward_for_example function. This function takes as input (1) the treatment outcomes for a given example, and (2) a dictionary containing settings of the parameters required to compute the reward. It returns a Pandas Series containing the computed rewards for each outcome.

To run an experiment, run the policy_learning.py script with the appropriate settings of the parameters (see descriptions in the Parameters section below).

Example scripts can be found in sample_validation_exp_script.sh and sample_test_exp_script.sh.

Parameters

Core Parameters (used across all approaches)

Parameter Description Type Required
--exp_name User-specified name for current experiment. str Yes
--mode Policy learning method to use. Can choose from [direct, thresholding, exp_reward_max] str Yes
--features_path Path to CSV containing features to be used as model inputs for learning policies. str Yes
--outcomes_path Path to CSV containing treatment outcomes for dataset of interest. str Yes
--validate Flag indicating if running evaluation on a validation set. If not provided, the library expects paths to data containing features / outcomes for a separate test set
--test_features_path Same as features_path, but contains data for test set. Required if validate flag is not provided str No
--test_outcomes_path Same as outcomes_path, but contains data for test set. Required if validate flag is not provided str Yes
--metadata_path Path to CSV containing any dataset metadata needed for running experiment (e.g., auxiliary information needed for generating appropriate train/validation splits) str No

Parameters for Indirect Approaches (Thresholding / Expected Reward Maximization)

Parameter Description Type Required
--best_models_path Path to JSON containing optimal model classes chosen during validation; map from outcome to name of optimal model class. Required if using an indirect approach in test mode. str Yes (test)
--best_hyperparams_path Path to JSON containing optimal hyperparameters for each outcome tuned during validation; map from outcome to hyperparameter setting. Required if using an indirect approach in test mode. str Yes (test)

Parameters for Thresholding

Parameter Description Type Required Default
--threshold_combos_path Path to JSON defining threshold search space used in thresholding approach. Map from each outcome to possible thresholds for that outcome. Required if using thresholding mode with validate flag set. See sample_threshold_space.json for example of such a file. str Yes
--threshold_selection_config_path Path to JSON specifying configuration for selecting optimal thresholds. Required if using thresholding mode with validate flag set. See sample_threshold_selection_config.json for an example. str Yes
--best_thresholds_path Path to . Required if using thresholding mode for evaluation on test set (i.e., without the validate flag). str Yes

Parameters for Direct Learning

Parameter Description Type Required Default
--reward_params_path Path to JSON defining reward parameter space. Map from each parameter in reward function to values at which we want to learn a policy. See sample_reward_space.json for an example. str Yes
--model_params_config_path Path to JSON containing parameters for training the direct model (e.g., learning rate, optimizer, etc.). Default values of parameters can be seen in direct/direct_policy_mode.py str No

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Code for "Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes" (KDD 2020)

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