ruoqi-liu / dsw

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 simulate_full.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 simulate_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