arXiv research

Paper introduces v-CMC linking causality and utility.

A new algorithm for robust causal discovery in small sample sizes.

The paper defines conditions for learning causal graphs from data with unobserved variables.

Paper characterizes and represents pairwise causal background knowledge for improved causal inference.

We propose a method to infer causal structures containing both discrete and continuous variables. The idea is to select causal hypotheses for which the conditional density of every variable, given its causes, becomes smooth. We define a family of smooth densities and conditional densities by second order exponential mo…

A new method reduces CI tests for causal structure learning.

Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when only single observations are present. We develop a theory how to generate causal grap…

Algorithm recovers causal graphs in presence of latent confounders and selection bias.

Study finds Value Granger-causes Size during crisis regimes but not during normal times.

We establish causal semantics for SDEs and develop methods to reason about them.

In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…

A new concept of causality for abstract phenomena.

New method estimates causal effects without knowing graph structure.

New approach reveals causal and probabilistic relationships from equations.

Local method identifies causal relations in Markov equivalent DAGs.

Method infers causal structure from system behaviors using RKHS and kernel $ε$-machines.

Develops a new framework for conditional independence.

A new method finds DAG models without ground truth.

Reframed GES uses a neural conditional dependence measure for consistent causal structure learning.

Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the assumption that all variables have been jointly measured in a single dat…

A new algorithm uses IVs to learn optimal policies from observational data.

Polynomial delay algorithm tests causal models with hidden variables.

This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been show…

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to…

New formalism for decision making combines causal structures with MDPs, improving reinforcement learning performance.

New algorithm learns causal structures by intersecting Markov blankets.

Proposes a weaker faithfulness assumption for causal discovery.

Polynomial-time methods count and sample DAGs from Markov classes.

New method identifies causal structure in exchangeable data.

Paper resolves conflicting Shapley value approaches by showing conditional is unsound and marginal is preferred.

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on fi…

Improves DRL for long-term causal inference with semiparametric methods.

We study 'meta-dependence' in conditional independence tests across different empirical distributions.

A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equivalence, based on conditional independence relations among the v…

The paper shows that relaxing assumptions about causal graphs can lead to exponentially large equivalence classes.

We consider the problem of inferring the directed, causal graph from observational data, assuming no hidden confounders. We take an information theoretic approach, and make three main contributions. First, we show how through algorithmic information theory we can obtain SCI, a highly robust, effective and computational…

New algorithm estimates causal effects for non-Gaussian data.

Forré introduces a new conditional independence notion for mixed variables.

PRL improves off-policy evaluation in partially observed MDPs.

Develops a model for causal discovery in path spaces.

This paper extends stable blanket theory to models with hidden variables and causal cycles.

New method tests causal relationships from data without needing to learn the entire graph.

New method reveals true causal functions in nonlinear time series, not just scores.

Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pas…

New method identifies nonstationary causal structures in time series data.

A new sampler improves the inference of causal structures from observational data.

This manuscript contributes a general and practical framework for casting a Markov process model of a system at equilibrium as a structural causal model, and carrying out counterfactual inference. Markov processes mathematically describe the mechanisms in the system, and predict the system's equilibrium behavior upon i…

Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Markov equivalent class. It is of great interest to describe and understand the space of such classes. However, with curren…