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KANITE: Kolmogorov-Arnold Networks for ITE estimation
We introduce KANITE, a framework leveraging Kolmogorov-Arnold Networks (KANs) for Individual Treatment Effect (ITE) estimation under multiple treatments setting in causal inference.
I See, Therefore I Do: Estimating Causal Effects for Image Treatments
In this work, we propose a model named NICE (Network for Image treatments Causal effect Estimation), for estimating individual causal effects when treatments are images.
Estimation of individual causal effects in network setup for multiple treatments
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.
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