Competition is the key: A Game Theoretic Causal Discovery Approach
Amartya Roy, Souvik Chakraborty
Paper
Details
#GameTheory #CausalDiscovery #MachineLearningResearch

Score‑Guided Strategies Boost Latent Variable Causal Discovery

BOSS‑FCI and GRaSP‑FCI add score‑based search to Fast Causal Inference, keeping correctness and improving scalability; FCIT trims independence tests dramatically. Read more: getnews.me/score-guided-strategies-... #causaldiscovery #latentvariables

GUIDE Framework Boosts AI-Driven Causal Discovery for Large Graphs

GUIDE pairs LLM adjacency priors with data, cutting runtime about 42% vs RL‑BIC/KCRL and boosting accuracy roughly 117% over NOTEARS and GraN‑DAG, on graphs with 70+ nodes. getnews.me/guide-framework-boosts-a... #causaldiscovery #guidelframework

Multi-View Causal Discovery Advances Without Non‑Gaussian Assumptions

Multi‑view SEM under mild inter‑view correlation extends DirectLiNGAM, PairwiseLiNGAM and ICA‑LiNGAM to identify acyclic causal graphs without non‑Gaussian assumptions. Read more: getnews.me/multi-view-causal-discov... #multiview #causaldiscovery

Near‑Optimal Experiment Design for Causal Discovery in Cyclic Models

Researchers present a near‑optimal design for cyclic models, using a greedy policy that selects interventions to shrink the causal graph; the reward is submodular. getnews.me/near-optimal-experiment-... #causaldiscovery #experimentdesign

New Kernel-Based Method Improves Conditional Independence Testing

Researchers introduced SplitKCI, an independence test that splits data—one subset fits the model, the other evaluates the statistic—to control false‑positives without losing power. getnews.me/new-kernel-based-method-... #splitkci #causaldiscovery

Quantile Partial Effect Enables New Causal Discovery Approach

Quantile Partial Effect (QPE) uses quantile regression to detect causal direction via a basis‑function test and ranks variables by Fisher Information in data. getnews.me/quantile-partial-effect-... #causaldiscovery #machinelearning

Client Challenge

Docs/PyPI: pypi.org/project/causal…

#CausalDiscovery #CausalInference

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach We introduce Causal Space-Time Stencil Learning (CaStLe) for learning local causal dynamical structure underlying space-time data CaStLe enables previously infeasible analyses of grid-cell-level ...

Published in JGR:MLC! We introduce CaStLe (Causal Space-Time Stencil Learning), a method for grid-level space-time causal discovery that scales efficiently in high-dimensional Earth system data. It enables causal analysis of grid-level processes like eruption plumes.

#EarthScience #CausalDiscovery

Forgot to add tags! #PyConAU and maybe also #CausalDiscovery, #CausalInference, #Causality

If you're interested in causal discovery and time series, you should definitely follow @jakobrunge.bsky.social who just joined the platform.

#CausalSky #causality #causaldiscovery #timeseries

We heard the science community has moved here. Looking for #causality , #causalinference #causaldiscovery and related #machinelearning accounts to follow. If that's you, please tag / follow us!

An introduction to causal discovery - Swiss Journal of Economics and Statistics In social sciences and economics, causal inference traditionally focuses on assessing the impact of predefined treatments (or interventions) on predefined outcomes, such as the effect of education pro...

Interested in #CausalDiscovery to uncover causality among multiple variables? Check out my introductory #OpenAccess article in the Swiss Journal of Economics & Statistics @sjeseditors.bsky.social (very grateful to editor @mariusbrulhart.bsky.social for his support): doi.org/10.1186/s419...

Causal discovery: An introduction | Andrew's Blog This post continues my exploration of causal inference, focusing on the type of problem an empirical researcher is most familiar with: where the underlying causal model is not known. In this case, the...

www.reid-lab.org/blog/21

Hi! Finally getting around to making new blog posts. Here's an introduction (by me, a rookie) into #CausalDiscovery approaches.

How can we use observational data to discover the underlying causal system?

Please comment &/or share!

#CausalInference #Stats #Neuroscience

Need ideas for causal discovery from time series data?

Check this (works with hidden confounding).

1/🧵

#causaldiscovery #CausalSky #machinelearning

What if we could discover the true causal structure from observational data?

Too good to be true?

1/n 👇🏼

#causality #CausalSky #causaldiscovery #machinelearning

Dance me to the end of DAG

Who knows the steps though?

Causal discovery is a task of recovering the information about the data
generating process from observational, interventional or mixed data
generated by this process.

1/n

#CausalSky #machinelearning #causality #causaldiscovery