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DSW

Introduction

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.

Requirement

Ubuntu16.04, python 3.6

Install pytorch 1.4

Data preprocessing

Synthetic dataset

Simulate the all covariates, treatments and outcomes

cd simulation
python synthetic.py

Semi-synthetic dataset

With a similar simulation process, we construct a semi-synthetic dataset based on a real-world dataset: MIMIC-III.

cd simulation
python synthetic_mimic.py

MIMIC-III dataset

Obtain the patients data of two treatment-outcome pairs: (1) vasopressor-Meanbp; (2) ventilator-SpO2.

cd simulation
python pre_mimic.py

Full list of covariates

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.

DSW

Running example

python train_synthetic.py --observation_window 30 --epochs 64 --batch-size 128 --lr 1e-3

Outputs

  • ITE estimation metrics: PEHE, ATE
  • Factual prediction metric: RMSE