CLEaR最新文献 - Book学术

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise 基于分数匹配的任意噪声加性模型的因果发现

CLEaR Pub Date : 2023-04-06 DOI: 10.48550/arXiv.2304.03265

Francesco Montagna, Nicoletta Noceti, L. Rosasco, Kun Zhang, Francesco Locatello

引用次数: 5

Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model 分类数据的因果辨析:统一渠道模型

CLEaR Pub Date : 2023-03-14 DOI: 10.48550/arXiv.2303.08572

Mário A. T. Figueiredo, Catarina A. Oliveira

{"title":"Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model","authors":"Mário A. T. Figueiredo, Catarina A. Oliveira","doi":"10.48550/arXiv.2303.08572","DOIUrl":"https://doi.org/10.48550/arXiv.2303.08572","url":null,"abstract":"Distinguishing cause from effect using observations of a pair of random variables is a core problem in causal discovery. Most approaches proposed for this task, namely additive noise models (ANM), are only adequate for quantitative data. We propose a criterion to address the cause-effect problem with categorical variables (living in sets with no meaningful order), inspired by seeing a conditional probability mass function (pmf) as a discrete memoryless channel. We select as the most likely causal direction the one in which the conditional pmf is closer to a uniform channel (UC). The rationale is that, in a UC, as in an ANM, the conditional entropy (of the effect given the cause) is independent of the cause distribution, in agreement with the principle of independence of cause and mechanism. Our approach, which we call the uniform channel model (UCM), thus extends the ANM rationale to categorical variables. To assess how close a conditional pmf (estimated from data) is to a UC, we use statistical testing, supported by a closed-form estimate of a UC channel. On the theoretical front, we prove identifiability of the UCM and show its equivalence with a structural causal model with a low-cardinality exogenous variable. Finally, the proposed method compares favorably with recent state-of-the-art alternatives in experiments on synthetic, benchmark, and real data.","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114372582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding 估计短期实验和长期观测数据的长期因果效应，伴有未观察到的混淆

CLEaR Pub Date : 2023-02-21 DOI: 10.48550/arXiv.2302.10625

Graham W. Van Goffrier, Lucas Maystre, Ciarán M. Gilligan-Lee

{"title":"Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding","authors":"Graham W. Van Goffrier, Lucas Maystre, Ciarán M. Gilligan-Lee","doi":"10.48550/arXiv.2302.10625","DOIUrl":"https://doi.org/10.48550/arXiv.2302.10625","url":null,"abstract":"Understanding and quantifying cause and effect is an important problem in many domains. The generally-agreed solution to this problem is to perform a randomised controlled trial. However, even when randomised controlled trials can be performed, they usually have relatively short duration's due to cost considerations. This makes learning long-term causal effects a very challenging task in practice, since the long-term outcome is only observed after a long delay. In this paper, we study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Previous work provided an estimation strategy to determine long-term causal effects from such data regimes. However, this strategy only works if one assumes there are no unobserved confounders in the observational data. In this paper, we specifically address the challenging case where unmeasured confounders are present in the observational data. Our long-term causal effect estimator is obtained by combining regression residuals with short-term experimental outcomes in a specific manner to create an instrumental variable, which is then used to quantify the long-term causal effect through instrumental variable regression. We prove this estimator is unbiased, and analytically study its variance. In the context of the front-door causal structure, this provides a new causal estimator, which may be of independent interest. Finally, we empirically test our approach on synthetic-data, as well as real-data from the International Stroke Trial.","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131261790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

On the Interventional Kullback-Leibler Divergence 论介入性Kullback-Leibler散度

CLEaR Pub Date : 2023-02-10 DOI: 10.48550/arXiv.2302.05380

J. Wildberger, Siyuan Guo, Arnab Bhattacharyya, B. Scholkopf

{"title":"On the Interventional Kullback-Leibler Divergence","authors":"J. Wildberger, Siyuan Guo, Arnab Bhattacharyya, B. Scholkopf","doi":"10.48550/arXiv.2302.05380","DOIUrl":"https://doi.org/10.48550/arXiv.2302.05380","url":null,"abstract":"Modern machine learning approaches excel in static settings where a large amount of i.i.d. training data are available for a given task. In a dynamic environment, though, an intelligent agent needs to be able to transfer knowledge and re-use learned components across domains. It has been argued that this may be possible through causal models, aiming to mirror the modularity of the real world in terms of independent causal mechanisms. However, the true causal structure underlying a given set of data is generally not identifiable, so it is desirable to have means to quantify differences between models (e.g., between the ground truth and an estimate), on both the observational and interventional level. In the present work, we introduce the Interventional Kullback-Leibler (IKL) divergence to quantify both structural and distributional differences between models based on a finite set of multi-environment distributions generated by interventions from the ground truth. Since we generally cannot quantify all differences between causal models for every finite set of interventional distributions, we propose a sufficient condition on the intervention targets to identify subsets of observed variables on which the models provably agree or disagree.","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115614447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Causal Models with Constraints 带约束的因果模型

CLEaR Pub Date : 2023-01-17 DOI: 10.48550/arXiv.2301.06845

Sander Beckers, J. Halpern, Christopher R. Hitchcock

引用次数: 0

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling 基于稀疏机制移位建模的单细胞因果表示学习

CLEaR Pub Date : 2022-11-07 DOI: 10.48550/arXiv.2211.03553

Romain Lopez, Natavsa Tagasovska, Stephen Ra, K. Cho, J. Pritchard, A. Regev

{"title":"Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling","authors":"Romain Lopez, Natavsa Tagasovska, Stephen Ra, K. Cho, J. Pritchard, A. Regev","doi":"10.48550/arXiv.2211.03553","DOIUrl":"https://doi.org/10.48550/arXiv.2211.03553","url":null,"abstract":"Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the interpretability of latent variables as biological processes that define a cell's identity. Outside of biological applications, this problem is commonly referred to as learning disentangled representations. Although several disentanglement-promoting variants of the VAE were introduced, and applied to single-cell genomics data, this task has been shown to be infeasible from independent and identically distributed measurements, without additional structure. Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic. Here, we extend the application of these methodological advances to the analysis of single-cell genomics data with genetic or chemical perturbations. More precisely, we propose a deep generative model of single-cell gene expression data for which each perturbation is treated as a stochastic intervention targeting an unknown, but sparse, subset of latent variables. We benchmark these methods on simulated single-cell data to evaluate their performance at latent units recovery, causal target identification and out-of-domain generalization. Finally, we apply those approaches to two real-world large-scale gene perturbation data sets and find that models that exploit the sparse mechanism shift hypothesis surpass contemporary methods on a transfer learning task. We implement our new model and benchmarks using the scvi-tools library, and release it as open-source software at https://github.com/Genentech/sVAE.","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115638576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Backtracking Counterfactuals 回溯反设事实

CLEaR Pub Date : 2022-11-01 DOI: 10.48550/arXiv.2211.00472

Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers

{"title":"Backtracking Counterfactuals","authors":"Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers","doi":"10.48550/arXiv.2211.00472","DOIUrl":"https://doi.org/10.48550/arXiv.2211.00472","url":null,"abstract":"Counterfactual reasoning -- envisioning hypothetical scenarios, or possible worlds, where some circumstances are different from what (f)actually occurred (counter-to-fact) -- is ubiquitous in human cognition. Conventionally, counterfactually-altered circumstances have been treated as\"small miracles\"that locally violate the laws of nature while sharing the same initial conditions. In Pearl's structural causal model (SCM) framework this is made mathematically rigorous via interventions that modify the causal laws while the values of exogenous variables are shared. In recent years, however, this purely interventionist account of counterfactuals has increasingly come under scrutiny from both philosophers and psychologists. Instead, they suggest a backtracking account of counterfactuals, according to which the causal laws remain unchanged in the counterfactual world; differences to the factual world are instead\"backtracked\"to altered initial conditions (exogenous variables). In the present work, we explore and formalise this alternative mode of counterfactual reasoning within the SCM framework. Despite ample evidence that humans backtrack, the present work constitutes, to the best of our knowledge, the first general account and algorithmisation of backtracking counterfactuals. We discuss our backtracking semantics in the context of related literature and draw connections to recent developments in explainable artificial intelligence (XAI).","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114239698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A Meta-Reinforcement Learning Algorithm for Causal Discovery 因果发现的元强化学习算法

CLEaR Pub Date : 2022-07-18 DOI: 10.48550/arXiv.2207.08457

A. Sauter, Erman Acar, Vincent François-Lavet

{"title":"A Meta-Reinforcement Learning Algorithm for Causal Discovery","authors":"A. Sauter, Erman Acar, Vincent François-Lavet","doi":"10.48550/arXiv.2207.08457","DOIUrl":"https://doi.org/10.48550/arXiv.2207.08457","url":null,"abstract":"Causal discovery is a major task with the utmost importance for machine learning since causal structures can enable models to go beyond pure correlation-based inference and significantly boost their performance. However, finding causal structures from data poses a significant challenge both in computational effort and accuracy, let alone its impossibility without interventions in general. In this paper, we develop a meta-reinforcement learning algorithm that performs causal discovery by learning to perform interventions such that it can construct an explicit causal graph. Apart from being useful for possible downstream applications, the estimated causal graph also provides an explanation for the data-generating process. In this article, we show that our algorithm estimates a good graph compared to the SOTA approaches, even in environments whose underlying causal structure is previously unseen. Further, we make an ablation study that shows how learning interventions contribute to the overall performance of our approach. We conclude that interventions indeed help boost the performance, efficiently yielding an accurate estimate of the causal structure of a possibly unseen environment.","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116888096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Towards efficient representation identification in supervised learning 监督学习中高效表征识别的研究

CLEaR Pub Date : 2022-04-10 DOI: 10.48550/arXiv.2204.04606

Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas

{"title":"Towards efficient representation identification in supervised learning","authors":"Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas","doi":"10.48550/arXiv.2204.04606","DOIUrl":"https://doi.org/10.48550/arXiv.2204.04606","url":null,"abstract":"Humans have a remarkable ability to disentangle complex sensory inputs (e.g., image, text) into simple factors of variation (e.g., shape, color) without much supervision. This ability has inspired many works that attempt to solve the following question: how do we invert the data generation process to extract those factors with minimal or no supervision? Several works in the literature on non-linear independent component analysis have established this negative result; without some knowledge of the data generation process or appropriate inductive biases, it is impossible to perform this inversion. In recent years, a lot of progress has been made on disentanglement under structural assumptions, e.g., when we have access to auxiliary information that makes the factors of variation conditionally independent. However, existing work requires a lot of auxiliary information, e.g., in supervised classification, it prescribes that the number of label classes should be at least equal to the total dimension of all factors of variation. In this work, we depart from these assumptions and ask: a) How can we get disentanglement when the auxiliary information does not provide conditional independence over the factors of variation? b) Can we reduce the amount of auxiliary information required for disentanglement? For a class of models where auxiliary information does not ensure conditional independence, we show theoretically and experimentally that disentanglement (to a large extent) is possible even when the auxiliary information dimension is much less than the dimension of the true latent representation.","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"486 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123396931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Estimating Social Influence from Observational Data 从观测数据估计社会影响

CLEaR Pub Date : 2022-03-24 DOI: 10.48550/arXiv.2204.01633

Dhanya Sridhar, C. D. Bacco, D. Blei

{"title":"Estimating Social Influence from Observational Data","authors":"Dhanya Sridhar, C. D. Bacco, D. Blei","doi":"10.48550/arXiv.2204.01633","DOIUrl":"https://doi.org/10.48550/arXiv.2204.01633","url":null,"abstract":"We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The key challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.","PeriodicalId":171742,"journal":{"name":"CLEaR","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128672910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3