counterfactual_recurrent_network

Estimating counterfactual treatment outcomes over time through adversarially balanced representations

Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar

International Conference on Learning Representations (ICLR) 2020

Last Updated Date: January 15th 2020

Code Author: Ioana Bica (ioana.bica95@gmail.com)

The CRN (Counterfactual Recurrent Network) estimates the effects of treatments assigned over time. The CRN model consists of an encoder performing one-step ahead prediction of treatment responses and a decoder estimating the effects of sequences of treatments.

Outputs:

• RMSE for one-step-ahead prediction.

• RMSE for five-step-ahead prediction.

• Trained encoder and decoder models.

The CRN uses data from a Pharmacokinetic-Pharmacodynamic model of tumour growth which which is a bio-mathematical model that simulates the combined effects of chemotherapy and radiotherapy in non-small cell lung cancer patients (Geng et al 2017, https://www.nature.com/articles/s41598-017-13646-z). The same simulation model was used by Lim et al. 2018 (https://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks.pdf) We adopt their implementation from: https://github.com/sjblim/rmsn_nips_2018 and extend it to incorporate counterfactual outcomes.

The chemo_coeff and radio_coeff in the simulation specify the amount of time-dependent confounding for the chemotherapy and radiotherapy applications. The results in the paper were obtained by varying the chemo_coeff and radio_coeff, and thus obtaining different datasets. For gamma=chemo_coeff=radio_coeff in {1, 2, 3, 4, 5} there are results for both one-step-ahead prediction and sequence prediction in the paper (Figure 4).

The synthetic dataset for each setting of chemo_coeff and radio_coeff is over 1GB in size, which is why it is re-generated every time the code is run.

To test the Counterfactual Recurrent Network, run (this will use a default settings of hyperparameters):

python test_crn.py --chemo_coeff=2 --radio_coeff=2 --model_name=crn_test_2

To perform hyperparameter optimization and test the Counterfactual Recurrent Network , run:

python test_crn.py --chemo_coeff=2 --radio_coeff=2 --model_name=crn_test_2 --b_encoder_hyperparm_tuning=True --b_decoder_hyperparm_tuning=True

For the results in the paper, hyperparameter optimization was run (this can take about 8 hours on an NVIDIA Tesla K80 GPU).