Geometric derivation of Einstein equations from causal fermion systems.
problem Deriving Einstein's equations from a new theoretical framework.
method Analysis of causal fermion systems and causal action principle.
result Einstein equations derived from causal action principle.
We derive a consistent differential representation for the dynamics of a self-financing portfolio for different hedging strategies. In the basis of the derivation there is the so called "retarded action principle", which represents the causality in the evolution of dependent stochastic variables. We demonstrate this pr…
A new concept of causality for abstract phenomena.
problem Unclear definition of causality in real-life variables.
method Introduces 'phenomenological causality' based on elementary actions.
result Defines causal structure without hard-wired links.
Proves a positive mass theorem for static causal fermion systems.
problem Defining mass for complex spacetimes without regularity assumptions.
method Surface layer integrals comparing asymptotically flat and vacuum spacetimes.
result Proves a positive mass theorem for static causal fermion systems.
A new framework for robot block-stacking tasks using causal probabilistic models.
problem Robots fail outside controlled environments due to uncertainty and lack of explicit design for all scenarios.
method Causal probabilistic framework combining causal models and probabilistic representations of noise.
result Robots can perceive, reason about, and explain their environment for block-stacking tasks.
Reinterprets Granger causality with causal Bayesian networks and Reichenbach's principles.
problem Lack of a rigorous causal foundation in Granger causality.
method Reinterpreting Granger causality through Reichenbach's principles and causal Bayesian networks, implementing as c-GC.
result c-GC provides a more principled framework for causal discovery in observational datasets.
Recently, it has been shown how sampling actions from the predictive distribution over the optimal action-sometimes called Thompson sampling-can be applied to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution can then be constructed b…
New mass inequalities and proofs for causal variational principles.
problem Proving new mass inequalities for causal variational principles.
method Proved a new inequality for minimizers of causal variational principles and applied it to prove the positive mass theorem.
result Introduced a positive quasilocal mass and proved new mass inequalities.
New causal versions of MaxEnt and PIR avoid paradoxical probability updates.
problem Paradoxical probability updates in causal MaxEnt and PIR.
method Separate constraints into cause-specific and mechanism-specific restrictions.
result Causal MaxEnt avoids paradoxical updates and aligns with Information Geometric Causal Inference.
Unified approach to causal representation learning using invariance principles.
problem Identifying latent causal variables from high-dimensional observations.
method Guiding identification of causal variables with invariance principles rather than causal hierarchies.
result Unified method that mixes causal and non-causal assumptions improves treatment effect estimation.
Optimizes portfolios by identifying causal drivers of diversification.
problem Achieving efficient portfolio optimization based on asset and diversification dynamics.
method Commonality Principle, Reichenbach Common Cause Principle, conformal maps, Bayesian networks, correlation-based algorithms, neural networks, SDEs.
result Optimal portfolio diversification achieved through causal methodologies and sensitivity forecasting.
How do organisms recognize their environment by acquiring knowledge about the world, and what actions do they take based on this knowledge? This article examines hypotheses about organisms' adaptation to the environment from machine learning, information-theoretic, and thermodynamic perspectives. We start with construc…
Causal Bayesian networks interpret actions as interventions to connect models to real-world outcomes.
problem Connecting causal model predictions to real-world outcomes.
method Formal framework to interpret actions as interventions and prove impossibility results.
result No non-circular interpretation exists that satisfies natural desiderata without violating some.
Enhances optimization in multi-source settings with causal principles.
problem Optimizing functions with multiple sources of data and causal dependencies.
method Integrates Multi-Source Bayesian Optimization with Causal Bayesian Optimization principles.
result Improves optimization efficiency and reduces computational complexity.
Two environments are enough to infer causal graphs and counterfactuals.
problem Inferring causal relations from multiple environments, especially for nonlinear mechanisms.
method Using structural causal models and the invariance principle, the study shows that only two auxiliary environments are sufficient for causal graph inference and counterfactual inference.
result Two auxiliary environments are sufficient for identifying causal graphs and counterfactuals.
A method to approximate causal models using information theory.
problem Inferring causal direction and effect between discrete variables.
method Embedding distributions into a higher dimensional space and solving a linear optimization problem.
result Information-theoretic approximation (IACM) can be used for causal discovery in bivariate, discrete cases.
This research tackles intervention-centric causal reasoning in learning agents by using meta-learning.
problem Learning agents lack the concept of interventions, making causal learning challenging.
method A meta-reinforcement learning algorithm is used to learn causal relationships from observational data.
result The approach enables agents to learn and manipulate the environment effectively.
PESCAL uses mediators to learn from confounded offline data.
problem Learning from confounded observational data in reinforcement learning.
method PESCAL uses mediator variables and the pessimistic principle to address confounding bias and distributional shift.
result It is sufficient to learn a lower bound of the mediator distribution function to mitigate distributional shift.
Paper reconciles RCM and SCM frameworks for causal inference.
problem Clarifying the relationship between RCM and SCM frameworks.
method Neutral logical perspective, previous work, and abstract representation.
result Every RCM emerges as an abstraction of some representable RCM.
New method falsifies causal graphs using outlier events.
problem Inferring causal relationships from data is hard.
method Falsify candidate causal graphs based on outlier propagation.
result Statistical tests control false positives and have power guarantees.
New method for disentangling latent factors with sparse dependencies.
problem Disentangling latent factors from observed variables and past factors.
method Mechanism sparsity regularization and sparse causal graphical model.
result Identifiability of latent factors up to a sparse causal graph.
The MAXENT principle helps merge datasets to infer causal effects.
problem Inferring causal effects from unobserved variables.
method Using the maximum entropy principle with causal sufficiency and faithfulness assumptions.
result Identifies causal edges among variables from merged datasets.
AR CI framework handles complex confounders and sequential actions.
problem Low-dimensional confounders and singleton actions in causal inference.
method Sequencification to transform data into sequences, enabling CI with complex confounders and sequential actions.
result AR model can estimate multiple causal quantities using a single model, simplifying inference and improving outcome prediction.
New probabilistic approaches offer recourse recommendations even when causal models are imperfect.
problem Limited causal knowledge makes guaranteeing algorithmic recourse impossible.
method Two probabilistic approaches: Bayesian model averaging and average effect computation.
result Probabilistic approaches lead to more reliable recourse recommendations.
Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method f…
Based on conservation laws for surface layer integrals for critical points of causal variational principles, it is shown how jet spaces can be endowed with an almost-complex structure. We analyze under which conditions the almost-complex structure can be integrated to a canonical complex structure. Combined with the sc…
New framework improves model robustness by focusing on stable relations across environments.
problem Standard supervised learning fails under data distribution shift.
method Gradient-based learning framework derived from the principle of independent causal mechanisms (ICM).
result Models generalize well to unseen scenarios, ignoring unstable relations.
Reduction principles for proper actions on smooth manifolds.
problem Proper actions on smooth manifolds and their properties.
method Exhibit constructions and prove reduction principles for proper actions.
result Reduction principles hold for proper actions, polar actions, and copolarity.
Framework improves CATE estimation by aligning active learning with causal objectives.
problem High cost of outcome measurements limits CATE estimation.
method Causal-EPIG framework, targeting unobservable causal quantities.
result Strategies outperform standard baselines, revealing context-dependent optimal approaches.
New method identifies causal variables from partially observed data.
problem Learning from unpaired observations with instance-dependent partial observability.
method Proposes two methods enforcing sparsity in the inferred representation.
result Establishes two identifiability results for linear and piecewise linear mixing functions.
New method identifies causal graphs with limited data and noise.
problem Identifying causal graphs from observational data is generally impossible.
method Using additional data from two environments with different noise statistics, and assuming Gaussian noise.
result The entire causal graph can be uniquely identified with a constant number of environments.
A textbook on machine learning explaining patterns, predictions, and actions.
problem Understanding patterns in data for predictions and actions.
method Explains foundations of decision making, representation, optimization, and generalization.
result Equips readers with tools to reason about actions and their consequences.
Holographic principle matches deformed Liouville theory action.
problem Matching deformed Liouville theory action in holography.
method Developed a holographic scheme involving bending energy.
result Perfect match between deformed theory actions on field and gravity sides.
New method identifies common cause in causal insufficiency, revealing complex phase transitions.
problem Identifying common cause in causal insufficiency with observed joint probability.
method Generalized maximum likelihood method, closely related to maximum entropy principle.
result Identifies consistent common cause that aligns with the common cause principle.
New fairness criteria for algorithmic recourse actions that consider causal relationships.
problem Fairness of recourse actions in algorithmic classification.
method Proposes two new fairness criteria at group and individual levels, explicitly accounting for causal relationships.
result Fairness of recourse is complementary to fairness of prediction, and can be enforced by altering the classifier.
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…
Synthetic experiments are crucial for assessing causal machine learning methods.
problem Current empirical evaluations of causal machine learning methods are insufficient and unreliable.
method Propose principles for conducting rigorous empirical analyses with synthetic data.
result Rigorous synthetic experiments are essential for building trust in causal machine learning methods.
Local-to-global principle for Morse actions on symmetric spaces.
problem Recognizing Morse actions on symmetric spaces.
method Equivariant Morse quasiisometric embeddings of trees into symmetric spaces.
result Algorithmic recognizability of Morse actions and construction of Morse Schottky subgroups.
The paper explores how to apply causal knowledge across different datasets to improve learning.
problem How to apply causal knowledge across different datasets to improve learning.
method Investigates the structural causal bandit with transportability, fusing priors from source environments to enhance learning in the deployment setting.
result Achieves a sub-linear regret bound with an explicit dependence on informativeness of prior data, potentially outperforming standard bandit approaches.
Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.
problem Challenging traditional concepts of cause-effect relations in quantum mechanics.
method Introducing a general framework to estimate causal influences without interventions or classical/quantum assumptions.
result Every pure bipartite entangled state violates classical bounds on causal influence, negating the idea that Bell inequalities are the only signature of incompatibility.
Proposes MCBO for causal Bayesian optimization with model learning and regret bounds.
problem Maximizing downstream variables in unknown structural models.
method Model-based causal Bayesian optimization (MCBO) that learns full system models and trades off exploration and exploitation.
result First non-asymptotic bounds for CBO and practical implementation showing superior performance.
New methods use vector search and nearest-neighbor matching for policy learning in causal inference.
problem Learning optimal policies in causal inference with limited data.
method RAG-based policy learning with vector search and nearest-neighbor matching.
result The methods bound the within-candidate choice regret and evaluate the one-step method directly as a policy.
In this work, a version of Fermat's principle for causal curves with the same energy in time orientable Finsler spacetimes is proved. We calculate the secondvariation of the {\it time arrival functional} along a geodesic in terms of the index form associated with the Finsler spacetime Lagrangian. Then the character of …
Framework identifies causal direction from single data setting.
problem Identify causal direction from single observational data.
method VCEI framework based on ICM principle and artificial variation.
result VCEI is competitive to other frameworks in identifying causal direction.
CDFM aims to unify causal discovery across diverse datasets.
problem Fragmented, test-driven causal discovery approaches struggle with modern data heterogeneity.
method CDFM is a unified, general-purpose framework using a variational decomposition of causal mechanisms.
result CDFM outperforms traditional algorithms across diverse datasets.
This paper improves causal inference using deep neural networks for low-dimensional covariates.
problem Improving causal inference with deep learning for high-dimensional covariates.
method Doubly robust off-policy learning with deep neural networks on low-dimensional manifolds.
result Nonasymptotic regret bounds for finite- and continuous-action scenarios, converging at a fast rate depending on intrinsic manifold dimension.
New model predicts drug effects across various cell types using causal imputation.
problem Predict drug effects across different cell types given limited data.
method Introduces a novel SCM-based model class with latent factor structure and uses Synthetic Interventions estimator.
result Method outperforms other matrix completion approaches in drug repurposing dataset.
A new method identifies causal direction using dense functional classes.
problem Determining causal direction between two univariate, continuous-valued variables.
method Minimum Description Length (MDL) principle applied to cubic regression splines.
result LCUBE method achieves superior precision in identifying causal direction.