Bayesian Federated Estimation of Causal Effects from Observational Data

Vo, T. V., Lee, Y., Hoang, T. N., & Leong, T. Y. (2022). Bayesian Federated Estimation of Causal Effects from Observational Data. In The 38th Conference on Uncertainty in Artificial Intelligence.
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Please cite:

@inproceedings{vo2022bayesian,
  title={Bayesian Federated Estimation of Causal Effects from Observational Data},
  author={Vo, Thanh Vinh and Lee, Young and Hoang, Trong Nghia and Leong, Tze-Yun},
  booktitle={The 38th Conference on Uncertainty in Artificial Intelligence},
  year={2022}
}

Table of Contents

• Requirements
• Import packages
• Prepare device: GPU or GPU
• Load data
• Convert numpy arrays to tensors
• Configure hyperparameters
• Train the model
• Some examples
• Some experimental results

Requirements

This code has been tested on:

gpytorch==1.3.0
pytorch==1.7.0+cu101

Import packages

import numpy as np
import torch
import pandas as pd
import gpytorch
from scipy import stats
from model_train import train_model
from model_utils import *
from evaluation import Evaluation

Prepare device: GPU or GPU

if torch.cuda.is_available():
  torch.set_default_tensor_type('torch.cuda.FloatTensor')
  print('Use ***GPU***')
  print(torch.cuda.get_device_properties(0).total_memory/1024/1024/1024,'GB')
else:
  print('Use CPU')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

If you have GPU, the above code would use it by default. Otherwise, it would use CPU.

Load data

There are 3 datasets: DATA-1, DATA-2, IHDP. Use the following codes to load IHDP dataset:

from load_datasets import IHDP
dataset = IHDP()
source_size = dataset.source_size
train_size = dataset.train_size
test_size = dataset.test_size
val_size = dataset.val_size

To load a new dataset, create a new class for that dataset in load_datasets.py. The class should be similar to that of IHDP.

Convert numpy arrays to tensors

• Let y, y_cf, x, w be numpy arrays of the training data.
• Let yte, y_cfte, xte, wte be numpy arrays of the training data. First we need to transform these numpy arrays into tensors:

# Tensors of training data
N = y.shape[0]
X = torch.from_numpy(x.reshape(N,-1)).float().to(device)
Yobs = torch.from_numpy(y.reshape(-1,1)).float().to(device)
W = torch.from_numpy(w.reshape(-1,1)).float().to(device)

# Tensors of testing data
Nte = yte.shape[0]
Xte = torch.from_numpy(xte.reshape(Nte,-1)).float().to(device)
Yobste = torch.from_numpy(yte.reshape(-1,1)).float().to(device)
Wte = torch.from_numpy(wte.reshape(-1,1)).float().to(device)

Configure hyperparameters

n_iterations = 2000
learning_rate = 1e-3
display_per_iter = 200

Train the model

model_server, model_sources = train_model(Yobs=Yobs, X=X, W=W, # for training
                                          Yobste=Yobste, Xte=Xte, Wte=Wte, # for testing
                                          y=y, y_cf=y_cf, x=x, w=w, # To print the errors
                                                                    # of ATE & ITE on training set
                                          yte=yte, y_cfte=y_cfte, xte=xte, wte=wte, # To print the errors
                                                                                    # of ATE & ITE on testing set
                                          n_sources=n_sources, train_size=train_size, test_size=test_size,
                                          n_iterations=n_iterations, learning_rate=learning_rate,
                                          display_per_iter=display_per_iter)

The above code would print out the errors of ATE and ITE on training and testing data for every display_per_iter iterations.

Some examples

We recommend to start with these examples:

• example_DATA-1.ipynb
• example_DATA-1.ipynb
• example_ihdp.ipynb

Some experimental results

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Federated causal inference analysis

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The impact of inter-dependency

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Contrasting with baselines

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The estimated distribution of ATE

Synthetic dataset

IHDP dataset