Code for the paper "Harnessing Causality in Reinforcement Learning With Bagged Decision Times".

• Base files that will be used for both running experiments for the proposed algorithm and estimating standardized treatment effect (STE).

  • /dataset.py: A container that stores the generated episodes.
  • /env_testbed.py: Implements the vanilla testbed. The files env_testbed_RE.py, env_testbed_AR.py, and env_testbed_RC.py are testbed variants that violate the assumptions in the DAG. env_testbed_MA.py allows interaction effects between $M_{d, 1:k-1}$ and $A_{d, k}$ on $M_{d, k}$.
  • /env_config_base.py: Base environment configurator.
  • /mrt.py: Runs a micro-randomized trial (MRT) that selects actions with a fixed probability in the vanilla testbed. The files mrt_RE.py, mrt_AR.py, mrt_RC.py, and mrt_MA.py run the MRT in different testbed variants.

• /experiments: Contains scripts to run experiments for BRLSVI, RLSVI, SRLSVI, and RAND.

  • /exp_BRLSVI.py, /exp_RLSVI.py, /exp_SRLSVI.py, and /exp_RAND.py run the experiments for the four algorithms.
  • /env_config.py, ..., /env_config4.py are the configurators for the four testbed variants in the paper. env_config_AR.py is the configurator for a testbed variant that violates the assumption $A_{d, 1:K} \to R_d$ in the DAG. env_config_MA.py is the configurator for a testbed variant that allows interaction effects between $M_{d, 1:k-1}$ and $A_{d, k}$.
  • artificial_data.py: Combines observed and artificial data (in the current experiments, no artificial data is used).
  • BRLSVI.py: Updates the policy in BRLSVI.
  • RLSVI.py: Updates the policy in RLSVI.
  • SRLSVI.py: Updates the policy in SRLSVI.
  • To compare different states, /exp_BRLSVI_Sp.py, /exp_BRLSVI_Spp.py, and /exp_BRLSVI_Sppp.py run the experiments with /BRLSVI_Sp.py, /BRLSVI_Spp.py, and /BRLSVI_Sppp.py, respectively.
  • eval.py: Compares different algorithms by drawing the figures.
  • run_BRLSVI.sh, run_RLSVI.sh, run_SRLSVI.sh, run_RAND.sh, and run_BRLSVIS.sh are scripts that submit their respective experiment code to the server.
  • /params_env_V2, /params_env_RE_V2.py, /params_env_AR_V2.py, /params_env_RC_V2.py, /params_env_MA_V2.py: Contain parameters for the vanilla testbed and other testbed variants. It preserves confidentiality via perturbations.
  • params_std_V2.json: Contains the standardization and truncation parameters.

• /ste: Estimates the STE for testbed variants.

  • /env_config_base.py: Base environment configurator. The parameter $W = 1$ means that all the bag-specific rewards are observed.
  • /opt_policy.py: Finds the true optimal policy of the vanilla testbed with a very large dataset. The files opt_policy_RE.py, opt_policy_AR.py, and opt_policy_RC.py find the true optimal policy of testbed variants that violate the assumptions in the DAG.
  • /eval_ste.py: Generates episodes under the optimal policy of the vanilla testbed and the zero policy. The files eval_ste_RE.py, eval_ste_AR.py, and eval_ste_RC.py generate episodes under the optimal policy of different testbed variants.
  • /env_config.py: Configurator for a testbed variant that enhances the positive effects by increasing $A_{d, k} \to M_{d, k}$.
  • /env_config2.py: Configurator for a testbed variant that enhances the negative effects by increasing $E_d \to R_d$ and decreasing $A_{d, k} \to E_d$.
  • /env_config3.py: Configurator for a testbed variant that enhances both the positive and the negative effects.
  • /env_config_AR.py: Configurator for a testbed variant that violates the assumption $A_{d, 1:K} \to R_d$ in the DAG and reduces the positive effect in $A_{d, 1:K} \to R_d$.
  • /ste_variants.py: Calculates the STE and draws the figures.
  • The testbed parameter files /params_env_V2, /params_env_RE_V2.py, /params_env_AR_V2.py, /params_env_RC_V2.py, params_std_V2.json need to be copied into this folder before running the code.