A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Instrumented data enables causal scientific machine learning
Behavior modification improves prediction accuracy by nudging user behavior.
PEARL uses reinforcement learning to improve matrix preconditioners.
Study evaluates counterfactual explanations using Pearl's method.
PEARL uses AI to replicate private equity performance with liquid assets.
The vision systems of the eagle and the snake outperform everything that we can make in the laboratory, but snakes and eagles cannot build an eyeglass or a telescope or a microscope. (Judea Pearl)
The study quantifies the information needed for causal queries at different levels of Pearl's hierarchy.
Paper tackles causal effect identification in sub-population with latent variables.
It is common practice in using regression type models for inferring causal effects, that inferring the correct causal relationship requires extra covariates are included or ``adjusted for''. Without performing this adjustment erroneous causal effects can be inferred. Given this phenomenon it is common practice to inclu…
Single proxy variable helps estimate causal effects from confounders.
VACA models graph data for causal inference without hidden confounders.
Efficiently infers interventional distributions from observational data.
How can we understand classification decisions made by deep neural networks? Many existing explainability methods rely solely on correlations and fail to account for confounding, which may result in potentially misleading explanations. To overcome this problem, we define the Causal Concept Effect (CaCE) as the causal e…
The purpose of this paper is to construct an example of a 2-knot wildly embedded in as the limit set of a Kleinian group. We find that this type of wild 2-knots has very interesting topological properties.
MpFL models clients as strategic players to reach equilibrium with less communication.
We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network on a graph with discrete variables and bounded in-degree and bounded `confounded components', we show that interventions on an unknown causal Bayesian ne…
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in the…
Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard …
Statistical tests for fairness in admissions data reveal hidden patterns.
PEARL combines multiple representation learning methods to enhance model performance.
We consider the numerical stability of the parameter recovery problem in Linear Structural Equation Model ($\LSEM$) of causal inference. A long line of work starting from Wright (1920) has focused on understanding which sub-classes of $\LSEM$ allow for efficient parameter recovery. Despite decades of study, this questi…
AI needs causal inference to avoid being just a correlation machine.
Causal normalizing flows recover causal models from observational data.
It seems to be a pearl of conventional wisdom that parameter learning in deep sum-product networks is surprisingly fast compared to shallow mixture models. This paper examines the effects of overparameterization in sum-product networks on the speed of parameter optimisation. Using theoretical analysis and empirical exp…
In an independence model, the triplets that represent conditional independences between singletons are called elementary. It is known that the elementary triplets represent the independence model unambiguously under some conditions. In this paper, we show how this representation helps performing some operations with in…
CausalVAE learns causal relationships in VAE models for better data disentanglement.
We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN. Both algorithms use statistical independence tests to infer the structure by successively constraining the set of structures consistent with the results of these tests. Until very recently, algorithms for structure lear…
We extend Andersson-Madigan-Perlman chain graphs by (i) relaxing the semidirected acyclity constraint so that only directed cycles are forbidden, and (ii) allowing up to two edges between any pair of nodes. We introduce global, and ordered local and pairwise Markov properties for the new models. We show the equivalence…
Causal discovery algorithms can help generate legal arguments.
We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one for dropped features. We argue that the confusion is based on not carefully distinguishing between observational and interventional conditi…
Estimates causal effects using neural autoregressive density estimators.
Latent tree models are graphical models defined on trees, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used in:…
In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "s…
New algorithm identifies causal relationships from graphs, even with selection bias.
Revisits causal inference identifiability with positivity assumption.
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, …
Relational Structural Causal Models enable causal reasoning about unseen object combinations.
New method identifies optimal subset of stable information to transfer for better model generalization.
This paper provides efficient algorithms for computing entropy and KL divergence in Bayesian networks.
We establish a foundation for multivariate counterfactual identification using dynamic optimal transport.
Deep SCMs with deep learning infer counterfactuals from noisy data.
An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem. However, existing methods have significant …
Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century. In its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the 1980s, and has si…
Paper simplifies complex causal identifiability problems with exogenous isomorphism.
New definition of patient-specific root causes of disease using counterfactuals.
Proposes a new method for algorithmic recourse in confounded settings.
Multicomponent bilayer structures arise as the ubiquitous plasma membrane in cellular biology and as blends of amphiphilic copolymers used in electrolyte membranes, drug delivery, and emulsion stabilization within the context of synthetic chemistry. We develop the multicomponent functionalized Cahn-Hilliard (mFCH) free…
Bayesian network models with latent variables are widely used in statistics and machine learning. In this paper we provide a complete algebraic characterization of Bayesian network models with latent variables when the observed variables are discrete and no assumption is made about the state-space of the latent variabl…