CausalTransformer

Causal Transformer for estimating counterfactual outcomes over time.

The project is built with following Python libraries:

1. Pytorch-Lightning - deep learning models
2. Hydra - simplified command line arguments management
3. MlFlow - experiments tracking

Installations

First one needs to make the virtual environment and install all the requirements:

pip3 install virtualenv
python3 -m virtualenv -p python3 --always-copy venv
source venv/bin/activate
pip3 install -r requirements.txt

MlFlow Setup / Connection

To start an experiments server, run:

mlflow server --port=5000

To access MlFLow web UI with all the experiments, connect via ssh:

ssh -N -f -L localhost:5000:localhost:5000 <username>@<server-link>

Then, one can go to local browser http://localhost:5000.

Experiments

Main training script is universal for different models and datasets. For details on mandatory arguments - see the main configuration file config/config.yaml and other files in configs/ folder.

Generic script with logging and fixed random seed is following (with training-type enc_dec, gnet, rmsn and multi):

PYTHONPATH=. CUDA_VISIBLE_DEVICES=<devices> 
python3 runnables/train_<training-type>.py +dataset=<dataset> +backbone=<backbone> exp.seed=10 exp.logging=True

Backbones (baselines)

One needs to choose a backbone and then fill the specific hyperparameters (they are left blank in the configs):

• Causal Transformer (this paper): runnables/train_multi.py +backbone=ct
• Encoder-Decoder Causal Transformer (this paper): runnables/train_enc_dec.py +backbone=edct
• Marginal Structural Models (MSMs): runnables/train_msm.py +backbone=msm
• Recurrent Marginal Structural Networks (RMSNs): runnables/train_rmsn.py +backbone=rmsn
• Counterfactual Recurrent Network (CRN): runnables/train_enc_dec.py +backbone=crn
• G-Net: runnables/train_gnet.py +backbone=gnet

Models already have best hyperparameters saved (for each model and dataset), one can access them via: +backbone/<backbone>_hparams/cancer_sim_<balancing_objective>=<coeff_value> or +backbone/<backbone>_hparams/mimic3_real=diastolic_blood_pressure.

For CT, EDCT, and CT, several adversarial balancing objectives are available:

• counterfactual domain confusion loss (this paper): exp.balancing=domain_confusion
• gradient reversal (originally in CRN, but can be used for all the methods): exp.balancing=grad_reverse

To train a decoder (for CRN and RMSNs), use the flag model.train_decoder=True.

To perform a manual hyperparameter tuning use the flags model.<sub_model>.tune_hparams=True, and then see model.<sub_model>.hparams_grid. Use model.<sub_model>.tune_range to specify the number of trials for random search.

Datasets

One needs to specify a dataset / dataset generator (and some additional parameters, e.g. set gamma for cancer_sim with dataset.coeff=1.0):

• Synthetic Tumor Growth Simulator: +dataset=cancer_sim
• MIMIC III Semi-synthetic Simulator (multiple treatments and outcomes): +dataset=mimic3_synthetic
• MIMIC III Real-world dataset: +dataset=mimic3_real

Before running MIMIC III experiments, place MIMIC-III-extract dataset (all_hourly_data.h5) to data/processed/

Example of running Causal Transformer on Synthetic Tumor Growth Generator with gamma = [1.0, 2.0, 3.0] and different random seeds (total of 30 subruns), using hyperparameters:

PYTHONPATH=. CUDA_VISIBLE_DEVICES=<devices> 
python3 runnables/train_multi.py -m +dataset=cancer_sim +backbone=ct +backbone/ct_hparams/cancer_sim_domain_conf=\'0\',\'1\',\'2\' exp.seed=10,101,1010,10101,101010

Updated results

Self- and cross-attention bug

New results for semi-synthetic and real-world experiments after fixing a bug with self- and cross-attentions (#7). Therein, the bug affected only Tables 1 and 2, and Figure 5 (https://arxiv.org/pdf/2204.07258.pdf). Nevertheless, the performance of the CT with the bug fixed did not change drastically.

Table 1 (updated). Results for semi-synthetic data for $\tau$-step-ahead prediction based on real-world medical data (MIMIC-III). Shown: RMSE as mean ± standard deviation over five runs.

	$\tau = 1$	$\tau = 2$	$\tau = 3$	$\tau = 4$	$\tau = 5$	$\tau = 6$	$\tau = 7$	$\tau = 8$	$\tau = 9$	$\tau = 10$
MSMs	0.37 ± 0.01	0.57 ± 0.03	0.74 ± 0.06	0.88 ± 0.03	1.14 ± 0.10	1.95 ± 1.48	3.44 ± 4.57	> 10.0	> 10.0	> 10.0
RMSNs	0.24 ± 0.01	0.47 ± 0.01	0.60 ± 0.01	0.70 ± 0.02	0.78 ± 0.04	0.84 ± 0.05	0.89 ± 0.06	0.94 ± 0.08	0.97 ± 0.09	1.00 ± 0.11
CRN	0.30 ± 0.01	0.48 ± 0.02	0.59 ± 0.02	0.65 ± 0.02	0.68 ± 0.02	0.71 ± 0.01	0.72 ± 0.01	0.74 ± 0.01	0.76 ± 0.01	0.78 ± 0.02
G-Net	0.34 ± 0.01	0.67 ± 0.03	0.83 ± 0.04	0.94 ± 0.04	1.03 ± 0.05	1.10 ± 0.05	1.16 ± 0.05	1.21 ± 0.06	1.25 ± 0.06	1.29 ± 0.06
EDCT (GR; $\lambda = 1$)	0.29 ± 0.01	0.46 ± 0.01	0.56 ± 0.01	0.62 ± 0.01	0.67 ± 0.01	0.70 ± 0.01	0.72 ± 0.01	0.74 ± 0.01	0.76 ± 0.01	0.78 ± 0.01
CT ($\alpha = 0$) (ours, fixed)	0.20 ± 0.01	0.38 ± 0.01	0.46 ± 0.01	0.50 ± 0.01	0.52 ± 0.01	0.54 ± 0.01	0.56 ± 0.01	0.57 ± 0.01	0.59 ± 0.01	0.60 ± 0.01
CT (ours, fixed)	0.21 ± 0.01	0.38 ± 0.01	0.46 ± 0.01	0.50 ± 0.01	0.53 ± 0.01	0.54 ± 0.01	0.55 ± 0.01	0.57 ± 0.01	0.58 ± 0.01	0.59 ± 0.01

Table 2 (updated). Results for experiments with real-world medical data (MIMIC-III). Shown: RMSE as mean ± standard deviation over five runs.

	$\tau = 1$	$\tau = 2$	$\tau = 3$	$\tau = 4$	$\tau = 5$
MSMs	6.37 ± 0.26	9.06 ± 0.41	11.89 ± 1.28	13.12 ± 1.25	14.44 ± 1.12
RMSNs	5.20 ± 0.15	9.79 ± 0.31	10.52 ± 0.39	11.09 ± 0.49	11.64 ± 0.62
CRN	4.84 ± 0.08	9.15 ± 0.16	9.81 ± 0.17	10.15 ± 0.19	10.40 ± 0.21
G-Net	5.13 ± 0.05	11.88 ± 0.20	12.91 ± 0.26	13.57 ± 0.30	14.08 ± 0.31
CT (ours, fixed)	4.60 ± 0.08	9.01 ± 0.21	9.58 ± 0.19	9.89 ± 0.21	10.12 ± 0.22

Figure 6 (updated). Subnetworks importance scores based on semi-synthetic benchmark (higher values correspond to higher importance of subnetwork connectivity via cross-attentions). Shown: RMSE differences between model with isolated subnetwork and full CT, means ± standard errors.

Last active entry zeroing bug

New results after fixing a bug with the synthetic tumor-growth simulator: outcome corresponding to the last entry for every time series was zeroed.

Table 9 (updated). Normalized RMSE for one-step-ahead prediction. Shown: mean and standard deviation over five runs (lower is better). Parameter $\gamma$ is the the amount of time-varying confounding: higher values mean larger treatment assignment bias.

	$\gamma = 0$	$\gamma = 1$	$\gamma = 2$	$\gamma = 3$	$\gamma = 4$
MSMs	1.091 ± 0.115	1.202 ± 0.108	1.383 ± 0.090	1.647 ± 0.121	1.981 ± 0.232
RMSNs	0.834 ± 0.072	0.860 ± 0.025	1.000 ± 0.134	1.131 ± 0.057	1.434 ± 0.148
CRN	0.755 ± 0.059	0.788 ± 0.057	0.881 ± 0.066	1.062 ± 0.088	1.358 ± 0.167
G-Net	0.795 ± 0.066	0.841 ± 0.038	0.946 ± 0.083	1.057 ± 0.146	1.319 ± 0.248
CT ($\alpha = 0$) (ours)	0.772 ± 0.051	0.783 ± 0.071	0.862 ± 0.052	1.062 ± 0.119	1.331 ± 0.217
CT (ours)	0.770 ± 0.049	0.783 ± 0.071	0.864 ± 0.059	1.098 ± 0.097	1.413 ± 0.259

Table 10 (updated). Normalized RMSE for $\tau$-step-ahead prediction (here: random trajectories setting). Shown: mean and standard deviation over five runs (lower is better). Parameter $\gamma$ is the amount of time-varying confounding: higher values mean larger treatment assignment bias.

	$\gamma = 0$	$\gamma = 1$	$\gamma = 2$	$\gamma = 3$	$\gamma = 4$
('2', 'MSMs')	0.975 ± 0.063	1.183 ± 0.146	1.428 ± 0.274	1.673 ± 0.431	1.884 ± 0.637
('2', 'RMSNs')	0.825 ± 0.057	0.851 ± 0.043	0.861 ± 0.078	0.993 ± 0.126	1.269 ± 0.294
('2', 'CRN')	0.761 ± 0.058	0.760 ± 0.037	0.805 ± 0.050	2.045 ± 1.491	1.209 ± 0.192
('2', 'G-Net')	1.006 ± 0.082	0.994 ± 0.086	1.185 ± 0.077	1.083 ± 0.145	1.243 ± 0.202
('2', 'CT ($\alpha = 0$) (ours)')	0.766 ± 0.029	0.781 ± 0.066	0.814 ± 0.078	0.944 ± 0.144	1.191 ± 0.316
('2', 'CT (ours)')	0.762 ± 0.028	0.781 ± 0.058	0.818 ± 0.091	1.001 ± 0.150	1.163 ± 0.233
('3', 'MSMs')	0.937 ± 0.060	1.133 ± 0.158	1.344 ± 0.262	1.525 ± 0.400	1.564 ± 0.545
('3', 'RMSNs')	0.824 ± 0.043	0.871 ± 0.036	0.857 ± 0.109	1.020 ± 0.140	1.267 ± 0.298
('3', 'CRN')	0.769 ± 0.057	0.777 ± 0.037	0.826 ± 0.077	1.789 ± 1.108	1.356 ± 0.330
('3', 'G-Net')	1.103 ± 0.092	1.097 ± 0.095	1.355 ± 0.107	1.225 ± 0.184	1.382 ± 0.242
('3', 'CT ($\alpha = 0$) (ours)')	0.766 ± 0.037	0.806 ± 0.060	0.828 ± 0.106	0.996 ± 0.185	1.335 ± 0.465
('3', 'CT (ours)')	0.762 ± 0.036	0.807 ± 0.056	0.838 ± 0.120	1.072 ± 0.196	1.283 ± 0.312
('4', 'MSMs')	0.845 ± 0.060	1.022 ± 0.149	1.196 ± 0.233	1.325 ± 0.363	1.308 ± 0.482
('4', 'RMSNs')	0.780 ± 0.046	0.834 ± 0.040	0.814 ± 0.123	0.988 ± 0.146	1.169 ± 0.269
('4', 'CRN')	0.734 ± 0.061	0.743 ± 0.037	0.805 ± 0.096	1.567 ± 0.825	1.327 ± 0.293
('4', 'G-Net')	1.092 ± 0.090	1.074 ± 0.098	1.385 ± 0.117	1.212 ± 0.202	1.358 ± 0.253
('4', 'CT ($\alpha = 0$) (ours)')	0.730 ± 0.042	0.776 ± 0.056	0.802 ± 0.119	0.983 ± 0.208	1.394 ± 0.563
('4', 'CT (ours)')	0.726 ± 0.041	0.777 ± 0.054	0.810 ± 0.128	1.075 ± 0.220	1.302 ± 0.356
('5', 'MSMs')	0.747 ± 0.056	0.896 ± 0.136	1.038 ± 0.210	1.128 ± 0.320	1.155 ± 0.448
('5', 'RMSNs')	0.717 ± 0.053	0.775 ± 0.041	0.747 ± 0.124	0.922 ± 0.141	1.057 ± 0.246
('5', 'CRN')	0.678 ± 0.062	0.692 ± 0.037	0.761 ± 0.104	1.410 ± 0.604	1.242 ± 0.239
('5', 'G-Net')	1.033 ± 0.086	1.014 ± 0.097	1.358 ± 0.118	1.160 ± 0.199	1.285 ± 0.242
('5', 'CT ($\alpha = 0$) (ours)')	0.673 ± 0.044	0.722 ± 0.052	0.748 ± 0.124	0.931 ± 0.213	1.405 ± 0.648
('5', 'CT (ours)')	0.669 ± 0.043	0.723 ± 0.053	0.751 ± 0.125	1.036 ± 0.238	1.264 ± 0.389
('6', 'MSMs')	0.647 ± 0.055	0.778 ± 0.123	0.894 ± 0.188	0.952 ± 0.284	1.060 ± 0.432
('6', 'RMSNs')	0.646 ± 0.058	0.702 ± 0.043	0.675 ± 0.121	0.847 ± 0.132	0.947 ± 0.225
('6', 'CRN')	0.614 ± 0.057	0.631 ± 0.035	0.706 ± 0.104	1.308 ± 0.438	1.132 ± 0.194
('6', 'G-Net')	0.963 ± 0.083	0.942 ± 0.090	1.321 ± 0.118	1.092 ± 0.183	1.195 ± 0.223
('6', 'CT ($\alpha = 0$) (ours)')	0.609 ± 0.042	0.657 ± 0.046	0.684 ± 0.122	0.864 ± 0.201	1.383 ± 0.699
('6', 'CT (ours)')	0.605 ± 0.040	0.657 ± 0.047	0.685 ± 0.119	0.979 ± 0.249	1.201 ± 0.419

Table 11 (updated). Normalized RMSE for $\tau$-step-ahead prediction (here: single sliding treatment setting). Shown: mean and standard deviation over five runs (lower is better). Parameter $\gamma$ is the amount of time-varying confounding: higher values mean larger treatment assignment bias.

	$\gamma = 0$	$\gamma = 1$	$\gamma = 2$	$\gamma = 3$	$\gamma = 4$
('2', 'MSMs')	1.362 ± 0.109	1.612 ± 0.172	1.939 ± 0.365	2.290 ± 0.545	2.468 ± 1.058
('2', 'RMSNs')	0.742 ± 0.043	0.760 ± 0.047	0.827 ± 0.056	0.957 ± 0.106	1.276 ± 0.240
('2', 'CRN')	0.671 ± 0.066	0.666 ± 0.052	0.741 ± 0.042	1.668 ± 1.184	1.151 ± 0.166
('2', 'G-Net')	1.021 ± 0.067	1.009 ± 0.092	1.271 ± 0.075	1.113 ± 0.149	1.257 ± 0.227
('2', 'CT ($\alpha = 0$) (ours)')	0.685 ± 0.050	0.679 ± 0.044	0.714 ± 0.053	0.875 ± 0.105	1.072 ± 0.315
('2', 'CT (ours)')	0.681 ± 0.052	0.677 ± 0.044	0.713 ± 0.042	0.908 ± 0.122	1.274 ± 0.366
('3', 'MSMs')	1.679 ± 0.132	1.953 ± 0.208	2.302 ± 0.437	2.640 ± 0.639	2.622 ± 1.132
('3', 'RMSNs')	0.783 ± 0.053	0.792 ± 0.047	0.889 ± 0.050	1.086 ± 0.175	1.382 ± 0.286
('3', 'CRN')	0.700 ± 0.078	0.692 ± 0.046	0.818 ± 0.051	1.959 ± 1.032	1.360 ± 0.225
('3', 'G-Net')	1.253 ± 0.079	1.226 ± 0.104	1.611 ± 0.102	1.383 ± 0.200	1.574 ± 0.328
('3', 'CT ($\alpha = 0$) (ours)')	0.707 ± 0.053	0.711 ± 0.038	0.770 ± 0.043	0.969 ± 0.119	1.261 ± 0.462
('3', 'CT (ours)')	0.703 ± 0.055	0.712 ± 0.040	0.770 ± 0.032	1.010 ± 0.119	1.536 ± 0.450
('4', 'MSMs')	1.871 ± 0.145	2.145 ± 0.227	2.489 ± 0.471	2.791 ± 0.681	2.615 ± 1.142
('4', 'RMSNs')	0.821 ± 0.079	0.837 ± 0.058	0.963 ± 0.106	1.216 ± 0.240	1.416 ± 0.304
('4', 'CRN')	0.734 ± 0.087	0.722 ± 0.041	0.898 ± 0.068	2.201 ± 0.967	1.573 ± 0.255
('4', 'G-Net')	1.390 ± 0.087	1.347 ± 0.112	1.819 ± 0.133	1.544 ± 0.243	1.769 ± 0.413
('4', 'CT ($\alpha = 0$) (ours)')	0.729 ± 0.056	0.749 ± 0.033	0.826 ± 0.046	1.053 ± 0.147	1.426 ± 0.574
('4', 'CT (ours)')	0.726 ± 0.057	0.748 ± 0.036	0.822 ± 0.036	1.089 ± 0.122	1.762 ± 0.523
('5', 'MSMs')	1.963 ± 0.155	2.221 ± 0.231	2.547 ± 0.479	2.810 ± 0.684	2.542 ± 1.122
('5', 'RMSNs')	0.855 ± 0.099	0.889 ± 0.074	1.030 ± 0.165	1.349 ± 0.326	1.434 ± 0.299
('5', 'CRN')	0.769 ± 0.094	0.755 ± 0.039	0.976 ± 0.082	2.361 ± 1.000	1.730 ± 0.292
('5', 'G-Net')	1.477 ± 0.092	1.430 ± 0.119	1.963 ± 0.157	1.667 ± 0.275	1.907 ± 0.471
('5', 'CT ($\alpha = 0$) (ours)')	0.758 ± 0.055	0.788 ± 0.036	0.875 ± 0.056	1.118 ± 0.172	1.560 ± 0.663
('5', 'CT (ours)')	0.756 ± 0.057	0.786 ± 0.039	0.870 ± 0.048	1.154 ± 0.111	1.922 ± 0.569
('6', 'MSMs')	1.970 ± 0.155	2.205 ± 0.228	2.509 ± 0.469	2.732 ± 0.662	2.422 ± 1.084
('6', 'RMSNs')	0.889 ± 0.112	0.936 ± 0.091	1.081 ± 0.211	1.473 ± 0.433	1.436 ± 0.290
('6', 'CRN')	0.807 ± 0.097	0.790 ± 0.035	1.047 ± 0.092	2.480 ± 1.078	1.827 ± 0.326
('6', 'G-Net')	1.538 ± 0.091	1.493 ± 0.121	2.062 ± 0.172	1.758 ± 0.286	1.994 ± 0.500
('6', 'CT ($\alpha = 0$) (ours)')	0.790 ± 0.058	0.827 ± 0.036	0.915 ± 0.063	1.177 ± 0.193	1.654 ± 0.704
('6', 'CT (ours)')	0.789 ± 0.059	0.821 ± 0.034	0.909 ± 0.054	1.205 ± 0.100	2.052 ± 0.608

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