Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand

no code implementations • 12 Feb 2024 • Md Musfiqur Rahman, Matt Jordan, Murat Kocaoglu

As an application of our algorithm, we evaluate two large conditional generative models that are pre-trained on the CelebA dataset by analyzing the strength of spurious correlations and the level of disentanglement they achieve.

Causal Inference Disentanglement

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference

no code implementations • 2 Jan 2024 • Md Musfiqur Rahman, Murat Kocaoglu

To address this, we propose a sequential training algorithm that, given the causal structure and a pre-trained conditional generative model, can train a deep causal generative model, which utilizes the pre-trained model and can provably sample from identifiable interventional and counterfactual distributions.

Causal Inference counterfactual