Estimation of individual causal effects in network setup for multiple treatments
This work addresses causal inference challenges in networked data for applications like social media analysis, but it is incremental as it extends existing methods to multiple treatments.
The paper tackles the problem of estimating individual treatment effects with multiple treatments and networked observational data by leveraging network information to infer hidden confounders, and it demonstrates superior performance over baselines on benchmark datasets like BlogCatalog and Flickr.
We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and networked observational data. Leveraging the network information, we aim to utilize hidden confounders that may not be directly accessible in the observed data, thereby enhancing the practical applicability of the strong ignorability assumption. To achieve this, we first employ Graph Convolutional Networks (GCN) to learn a shared representation of the confounders. Then, our approach utilizes separate neural networks to infer potential outcomes for each treatment. We design a loss function as a weighted combination of two components: representation loss and Mean Squared Error (MSE) loss on the factual outcomes. To measure the representation loss, we extend existing metrics such as Wasserstein and Maximum Mean Discrepancy (MMD) from the binary treatment setting to the multiple treatments scenario. To validate the effectiveness of our proposed methodology, we conduct a series of experiments on the benchmark datasets such as BlogCatalog and Flickr. The experimental results consistently demonstrate the superior performance of our models when compared to baseline methods.