Proof pending. Core topic summary fields are still materializing.
Causal discovery is a critical area of research focused on identifying causal relationships from data, particularly in complex systems where latent variables and high-dimensional data pose significant challenges. Recent advancements include frameworks that leverage deep learning, recursive decomposition, and intervention-based methods to improve the accuracy and efficiency of causal inference. Techniques like L2C and PACER enhance the ability to handle latent variables and large-scale interventional data, while approaches such as Causal-Audit assess the reliability of causal graphs under various assumption violations. These developments are essential for builders as they provide robust tools for making informed decisions based on causal insights, ultimately driving innovation across multiple fields, including healthcare, economics, and social sciences.
Topic-specific paper and score movement from the daily diff ledger.
Latent variables pose a fundamental challenge to causal discovery and inference. Conventional local methods focus on direct neighbors but fail to provide macro level insights. Cluster level methods en...
Leveraging deep learning for causal discovery in time series remains challenging because existing neural methods predominantly rely on component-wise architectures that fail to capture shared system d...
Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it computationally expensive in high-dimensional sett...
We propose SVAR-FM (Structural VAR with Flow Matching), a framework for time series causal discovery that treats a physics-based simulator as a mechanical realization of Pearl's do operator. Clamping ...
Understanding causal dependencies in observational data is critical for informing decision-making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acyclic Graphs (DAGs). ...
Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure learning can produce co...
Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs remains poorly understood. In particular, causal s...
Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensional settings where large-scale interventional data a...
Causal discovery aims to recover ``what causes what'', but classical constraint-based methods (e.g., PC, FCI) suffer from error propagation, and recent LLM-based causal oracles often behave as opaque,...
We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graph...
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Canonical route: /topics
Agent Handoff
Canonical ID causal-discovery | Route /topic/causal-discovery
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/causal-discoveryMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Causal Discovery",
"cluster": "Causal Discovery"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Causal Discovery",
"normalized_query": "causal-discovery",
"route": "/topic/causal-discovery",
"paper_ref": null,
"topic_slug": "causal-discovery",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.