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Found 17 papers, 11 papers with code
Independent Mechanism Analysis and the Manifold Hypothesis
As typical in ICA, previous work focused on the case with an equal number of latent components and observed mixtures.
CLadder: Assessing Causal Reasoning in Language Models
Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules.
Causal Component Analysis
As a corollary, this interventional perspective also leads to new identifiability results for nonlinear ICA -- a special case of CauCA with an empty graph -- requiring strictly fewer datasets than previous results.
Evaluating vaccine allocation strategies using simulation-assisted causal modelling
Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.
Probing the Robustness of Independent Mechanism Analysis for Representation Learning
One aim of representation learning is to recover the original latent code that generated the data, a task which requires additional information or inductive biases.
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Leveraging self-consistency, we show that the ELBO converges to a regularized log-likelihood.
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"
Model identifiability is a desirable property in the context of unsupervised representation learning.
Causal Inference Through the Structural Causal Marginal Problem
We introduce an approach to counterfactual inference based on merging information from multiple datasets.
Independent mechanism analysis, a new concept?
Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process.
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant.
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Learning explanations that are hard to vary
In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning.
Relative gradient optimization of the Jacobian term in unsupervised deep learning
Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.
Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects
We point out limitations and extensions for future work, and, finally, discuss the role of causal reasoning in the broader context of using AI to combat the Covid-19 pandemic.
Applications Methodology
Privacy-Preserving Causal Inference via Inverse Probability Weighting
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences.
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
In contrast to known identifiability results for nonlinear ICA, we prove that independent latent sources with arbitrary mixing can be recovered as long as multiple, sufficiently different noisy views are available.
Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines.
