Paper characterizes causal graphs from hard interventions and proposes a learning algorithm.
problem Discovering causal structure from hard interventions and observational data.
method Proposes graphical constraints and a learning algorithm based on do-calculus.
result Characterizes interventional equivalence classes of causal graphs with latent variables.
Paper relaxes faithfulness assumption for causal discovery using interventions.
problem Violation of faithfulness assumption in natural systems leads to incorrect causal structure identification.
method Use intervention-immediacy faithfulness assumption to identify causal structures with hard interventions.
result Interventions contain information about causal structure that can identify causal structures when faithfulness is violated.
Optimal intervention in economic networks modeled as influence maximization, with hard computational problems.
problem Optimal intervention in economic networks modeled as influence maximization.
method Transformed into influence maximization-like form, with theoretical and practical implications.
result Optimal intervention is NP-hard and cannot be approximated to a constant factor in polynomial time.
This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.
problem CRL under unknown multi-node interventions, focusing on single-node assumptions.
method Establishes identifiability results for general latent causal models under stochastic interventions.
result Identifiability up to ancestors using soft interventions, perfect identifiability using hard interventions.
GACBO optimizes unknown causal graphs with interventions.
problem Optimizing a target variable on an unknown causal graph with interventions.
method Graph Agnostic Causal Bayesian Optimisation (GACBO) seeks to balance exploitation and exploration of causal structures and functions.
result GACBO outperforms baselines in simulated and real-world applications.
Paper establishes identifiability and achievability for causal representation learning.
problem Identifying and recovering latent causal models and variables from observational and interventional data.
method Establishes identifiability and achievability using uncoupled interventions and a recovery algorithm.
result Guaranteed perfect recovery of latent causal model and variables under uncoupled interventions.
Efficient algorithms learn causal graphs with minimal interventions.
problem Learning causal relationships between observed variables in the presence of latents.
method Bi-criteria approximation goal combining intervention design and graph property testing.
result Achieve intervention cost within a small constant factor of the optimal.
We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove…
Introduces CStrees for modeling context-specific causal models from observational and interventional data.
problem Modeling context-specific causal relationships from mixed data types.
method Introduces CStrees with a novel factorization criterion and graphical characterization for context-specific conditional independence models.
result Derives a graphical characterization of model equivalence for observational CStrees and extends it to CStree models under context-specific interventions.
The paper tackles intervention generalization using factor graph models.
problem Generalizing from past experiments to novel conditions is hard due to combinatorial space of possible interventions.
method The approach is based on intervention factor models (IFMs) and factor graph models.
result Conditions for identifiability of expected outcomes of new regimes are derived.
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.
This paper tackles causal representation learning from multiple distributions without hard interventions.
problem Recovering latent causal variables and their relations from multiple distributions.
method Develops general solutions for causal representation learning without hard interventions, under sparsity constraints and suitable change conditions.
result Recovering the moralized graph of the underlying directed acyclic graph and latent variables related to the underlying causal model.
This paper tackles causal representation learning with linear and general transformations.
problem Identify and recover latent causal variables and graphs under unknown transformations.
method Score-based algorithms that use gradients of log-density functions for identifiability and achievability.
result Two stochastic hard interventions per node are sufficient for identifiability of general transformations.
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…
This paper identifies the minimal set of nodes for optimal conditional interventions in causal bandits.
problem Optimizing decision-making in causal bandits with conditional interventions.
method Graphical characterization and efficient algorithm to identify the minimal set of nodes.
result The proposed algorithm significantly prunes the search space and accelerates convergence rates.
Paper recovers latent causal structure and linear transformation from indirect observations.
problem Recovering latent causal structure and linear transformation from indirect observations.
method Established sufficient conditions for DAG recovery, leveraged score function properties, and used soft/hard interventions.
result Perfect recovery of latent DAG structure and linear transformation up to scaling using soft interventions, hard interventions with additional hypothesis testing.
A new method infers causal gene regulatory networks from parallel CRISPR interventions and transcriptomic data.
problem Learning causal gene regulatory networks from observational data is complicated by lack of identifiability and a combinatorial solution space.
method A continuous optimization framework that leverages observational and interventional data to infer a single causal structure, assuming a linear Structural Equation Model (SEM).
result A provably consistent estimator of the true DAG under mild assumptions.
This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.
problem Designing causal bandit algorithms with unknown causal graphs and stochastic intervention models.
method Establishes novel regret bounds and presents a computationally efficient algorithm for unknown graph and soft interventions.
result Regret bounds for unknown graph and soft interventions, with a universal minimax lower bound.
New algorithm identifies best intervention without graph knowledge.
problem Finding best intervention in causal bandit setting with unknown graph.
method Additive combinatorial linear bandit problem with action-elimination algorithm.
result One can identify best intervention without explicitly learning graph parents.
Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans extrapolate from past experience to new, unseen si…
The paper tackles causal bandits with unknown SCMs and soft interventions, providing upper and lower bounds on regret.
problem Optimizing interventions in a causal system with unknown SCMs and soft interventions.
method Assumes unknown SCMs from a general class, allows infinite interventions, and provides upper and lower bounds on regret.
result General upper and lower bounds on cumulative achievable regret for various SCMs.
Develops algorithms to balance personalization and statistical power in mobile health studies.
problem Balancing personalization and statistical power in mobile health studies.
method Develops general meta-algorithms to modify existing bandit algorithms.
result Guarantees sufficient power while improving user well-being.
The study quantifies the information needed for causal queries at different levels of Pearl's hierarchy.
problem How much additional information is needed for interventional and counterfactual queries compared to observational queries?
method Formalized via query-class description length, using Kolmogorov complexity of answer oracles induced by SCMs.
result Binary acyclic SCMs show a quadratic gap between observational and interventional descriptions, and a logarithmic gap between interventional and counterfactual descriptions.
Research aims to bridge statistical learning to causal models in AI.
problem Challenges in machine learning and AI related to causality.
method Transition from statistical learning to causal models.
result Progress in AI may require advances in causal modeling.
DeFi lending protocols faced challenges during Ethereum's merge, but avoided major liquidations.
problem Ethereum's merge caused volatility and potential liquidations in DeFi lending.
method Analyzed AAVE and Compound lending protocols during the merge and hard fork.
result Borrowing rates spiked but no significant liquidations occurred.
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.
PLIs improve classifier performance by fine-tuning latent representations.
problem Difficult interpretation of high-dimensional latent representations in neural networks.
method Back-propagation of manual changes to low-dimensional embeddings using t-distributed stochastic neighbourhood embeddings.
result Manual separation of class clusters in latent space enhances classifier performance.
New method recovers causal DAGs from general environments without strict assumptions.
problem Recovering causal DAGs from real-world data with varying distributions.
method Formalizes desiderata for causal representation learning in general environments, leveraging sufficient change conditions up to third-order derivatives.
result Fully recovers latent DAG and identifies latent variables up to minor indeterminacies under nonparametric mixing.
The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level within small groups or within sub-populations of large groups. Given the availabilit…
Estimates joint causal effects using single-variable interventions on nonlinear models.
problem Estimating joint causal effects from single-variable interventions.
method Identifiability result and practical estimator for decomposing causal effects.
result Joint effects can be inferred without joint interventional data for nonlinear additive models.
Financial markets are not random, but hard to predict due to hidden causes and strategic use.
problem Hard to predict financial markets
method Disciplined thesis on the distinction between no-arbitrage, informational efficiency, and net exploitability
result Discovers that markets are hard to predict due to hidden causes and strategic use
Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood. We present a practical Bayesian formulation for unsupervised and semi-supervised learning with GANs. Within this framework, we use stochastic gradient Hamilton…
This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.
problem Discovering the true causal graph from observational data with limited interventions.
method Proposes a stochastic intervention model and studies verification and search problems with approximation algorithms.
result Provides approximation algorithms with competitive ratios for verification and search problems.
Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…
Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change…
Paper proposes scalable algorithm to estimate intervention targets in linear models.
problem Estimating intervention targets in linear models from observational and interventional data.
method The paper proposes a scalable algorithm that estimates intervention sites from the difference between precision matrices of observational and interventional datasets.
result The algorithm consistently identifies all intervention targets and updates observational Markov equivalence classes to interventional ones.
New method disentangles mixed interventional and observational data in SEMs.
problem Learning causal relationships from mixed interventional and observational data.
method Developed a method to disentangle mixed interventional and observational data in linear SEMs with Gaussian noise.
result The method can identify causal graphs up to their interventional Markov Equivalence Class.
We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any human intervention. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each …
The paper presents a method to estimate joint interventional distributions from marginal interventional data.
problem Estimating joint interventional distributions from marginal interventional data.
method The paper extends the Causal Maximum Entropy method to use interventional data and employs Lagrange duality to prove the solution lies in the exponential family.
result The method allows for causal feature selection and inference of joint interventional distributions.
AGENTICAITA uses AI agents to autonomously trade markets without human intervention.
problem Inability of traditional trading systems to adapt to market complexity.
method Introduces an agentic AI framework with specialized LLM agents reasoning, negotiating, and acting.
result Demonstrated operational correctness and non-trivial inter-agent negotiation in live market conditions.
Bayesian method for causal discovery from unknown general interventions.
problem Learning causal DAGs from unknown interventions that modify parent sets.
method Bayesian approach with MCMC for approximating posterior DAGs and intervention targets.
result Bayesian method can identify DAGs and intervention targets up to equivalence classes.
Interventional data helps identify latent factors without distributional assumptions.
problem Identifying latent factors from interventional data without distributional assumptions.
method Leveraging geometric signatures of latent factors' support from interventional data.
result Latent causal factors can be identified up to permutation and scaling given data from perfect do-interventions.
IntDC framework uncovers causal relationships from non-interventional data.
problem Detecting causal relationships in non-interventional complex systems.
method Interventional Embedding Entropy (IEE) for causal strength measurement.
result IEE accurately finds causal edges and quantifies causal strength robustly.
Algorithm detects causal change points quickly with adaptive interventions.
problem Detecting changes in causal models with interventions.
method Centralization technique, Kullback-Leibler divergence for intervention selection, adaptive intervention policy.
result Theoretical first-order optimality and validation through simulations and real-world studies.
Method learns causal effects from multiple interventions in presence of unobserved confounders.
problem Disentangling causal effects from sets of interventions in the presence of unobserved confounders.
method Non-linear structural causal models with additive, multivariate Gaussian noise; algorithm that learns causal model parameters by pooling data from different regimes and maximizing combined likelihood.
result Identification proofs demonstrate that causal effects of single interventions can be learned from sets of interventions, even with unobserved confounders.
New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.
problem Discover causal relationships from data with adaptive interventions and node-dependent costs.
method Define new benchmarks and provide adaptive search algorithms for causal graph discovery.
result Logarithmic approximations achieved under various settings: atomic, bounded size interventions and generalized cost objectives.
We address the problem of optimal Central Bank intervention in the exchange rate market when interventions create feedback in the rate dynamics. In particular, we extend the work done on optimal impulse control by Cadenillas and Zapatero to incorporate temporary market reactions, of random duration and level, to Bank i…
GIT uses gradient estimators to target interventions for causal discovery.
problem Challenges in inferring causal structure from observational data.
method GIT uses gradient estimators to target interventions for causal discovery.
result GIT performs on par with competitive baselines, surpassing them in low-data regimes.