This repository contains source code for paper "Estimating Individual Treatment Effects with Time-Varying Confounders".
In this paper, we study the problem of Estimating individual treatment effects with time-varying confounders (as illustrated by a causal graph in the figure below)
We propose Deep Sequential Weighting (DSW) for estimating ITE with time-varying confounders. DSW consists of three main components: representation learning module, balancing module and prediction module.
To demonstrate the effectiveness of our framework, we conduct comprehensive experiments on synthetic, semi-synthetic and real-world EMR datasets (MIMIC-III). DSW outperforms state-of-the-art baselines in terms of PEHE and ATE.
Ubuntu16.04, python 3.6
Install pytorch 1.4
Simulate the all covariates, treatments and outcomes
cd simulation
python simulate_full.py
With a similar simulation process, we construct a semi-synthetic dataset based on a real-world dataset: MIMIC-III.
cd simulation
python simulate_mimic.py
We show the full list of static demographics and time-varying covariates of sepsis patients obtained from MIMIC-III.
| Category | Items | Type |
|---|---|---|
| Demographics | age | Cont. |
| gender | Binary | |
| race (white, black, hispanic, other) | Binary | |
| metastatic cancer | Binary | |
| diabetes | Binary | |
| height | Cont. | |
| weight | Cont. | |
| bmi | Cont. | |
| Vital signs | heart rate, systolic, mean and diastolic blood pressure | Cont. |
| Respiratory rate, SpO2 | Cont. | |
| Temperatures | Cont. | |
| Lab tests | sodium, chloride, magnesium | Cont. |
| glucose, BUN, creatinine, urineoutput, GCS | Cont. | |
| white blood cells count, bands, C-Reactive protein | Cont. | |
| hemoglobin, hematocrit, aniongap | Cont. | |
| platelets count, PTT, PT, INR | Cont. | |
| bicarbonate, lactate | Cont. |
python train_synthetic.py --observation_window 30 --epochs 64 --batch-size 128 --lr 1e-3
- ITE estimation metrics: PEHE, ATE
- Factual prediction metric: RMSE