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

• URL: http://arxiv.org/abs/2401.01426v2
• Date: Sun, 27 Oct 2024 04:18:04 GMT
• Title: Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
• Authors: Md Musfiqur Rahman, Murat Kocaoglu,
• Abstract summary: Modular-DCM is the first algorithm that, given the causal structure, uses adversarial training to learn the network weights. We show our algorithm's convergence on the COVIDx dataset and its utility with a causal invariant prediction problem on CelebA-HQ.
• Score: 5.522612010562183
• License:
• Abstract: Sound and complete algorithms have been proposed to compute identifiable causal queries using the causal structure and data. However, most of these algorithms assume accurate estimation of the data distribution, which is impractical for high-dimensional variables such as images. On the other hand, modern deep generative architectures can be trained to sample from high-dimensional distributions. However, training these networks are typically very costly. Thus, it is desirable to leverage pre-trained models to answer causal queries using such high-dimensional data. To address this, we propose modular training of deep causal generative models that not only makes learning more efficient, but also allows us to utilize large, pre-trained conditional generative models. To the best of our knowledge, our algorithm, Modular-DCM is the first algorithm that, given the causal structure, uses adversarial training to learn the network weights, and can make use of pre-trained models to provably sample from any identifiable causal query in the presence of latent confounders. With extensive experiments on the Colored-MNIST dataset, we demonstrate that our algorithm outperforms the baselines. We also show our algorithm's convergence on the COVIDx dataset and its utility with a causal invariant prediction problem on CelebA-HQ.

Related papers

• Unrolled denoising networks provably learn optimal Bayesian inference [54.79172096306631]
  We prove the first rigorous learning guarantees for neural networks based on unrolling approximate message passing (AMP) For compressed sensing, we prove that when trained on data drawn from a product prior, the layers of the network converge to the same denoisers used in Bayes AMP.
  arXiv  Detail & Related papers  (2024-09-19T17:56:16Z)
• Data Shapley in One Training Run [88.59484417202454]
  Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. Existing approaches require re-training models on different data subsets, which is computationally intensive. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
  arXiv  Detail & Related papers  (2024-06-16T17:09:24Z)
• Validation Diagnostics for SBI algorithms based on Normalizing Flows [55.41644538483948]
  This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. This work should help the design of better specified models or drive the development of novel SBI-algorithms.
  arXiv  Detail & Related papers  (2022-11-17T15:48:06Z)
• Learning Single-Index Models with Shallow Neural Networks [43.6480804626033]
  We introduce a natural class of shallow neural networks and study its ability to learn single-index models via gradient flow. We show that the corresponding optimization landscape is benign, which in turn leads to generalization guarantees that match the near-optimal sample complexity of dedicated semi-parametric methods.
  arXiv  Detail & Related papers  (2022-10-27T17:52:58Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Modeling Item Response Theory with Stochastic Variational Inference [8.369065078321215]
  We introduce a variational Bayesian inference algorithm for Item Response Theory (IRT) Applying this method to five large-scale item response datasets yields higher log likelihoods and higher accuracy in imputing missing data. The algorithm implementation is open-source, and easily usable.
  arXiv  Detail & Related papers  (2021-08-26T05:00:27Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• Testing for Typicality with Respect to an Ensemble of Learned Distributions [5.850572971372637]
  One-sample approaches to the goodness-of-fit problem offer significant computational advantages for online testing. The ability to correctly reject anomalous data in this setting hinges on the accuracy of the model of the base distribution. Existing methods for the one-sample goodness-of-fit problem do not account for the fact that a model of the base distribution is learned. We propose training an ensemble of density models, considering data to be anomalous if the data is anomalous with respect to any member of the ensemble.
  arXiv  Detail & Related papers  (2020-11-11T19:47:46Z)
• Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems [83.98774574197613]
  We take one of the simplest inference methods, a truncated max-product Belief propagation, and add what is necessary to make it a proper component of a deep learning model. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs) The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, optical flow and semantic segmentation.
  arXiv  Detail & Related papers  (2020-03-13T13:11:35Z)
• Learning Generative Models using Denoising Density Estimators [29.068491722778827]
  We introduce a new generative model based on denoising density estimators (DDEs) Our main contribution is a novel technique to obtain generative models by minimizing the KL-divergence directly. Experimental results demonstrate substantial improvement in density estimation and competitive performance in generative model training.
  arXiv  Detail & Related papers  (2020-01-08T20:30:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.