Research map / Probabilistic and scientific modeling
Causal discovery: research map
830 accepted papers on Causal discovery in Probabilistic and scientific modeling, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 10 approaches. The busiest year so far is 2026.
Within Probabilistic and scientific modeling, its share held steady from 11.4% in 2023–24 to 11.0% in 2025–26 (232 → 334 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
Explore Causal discovery in the interactive map
Working on something in this topic? Describe your idea in scime atlas to see which approach it falls under, the closest papers by meaning and how crowded the spot has become.
Approaches and key papers
counterfactual · causal inference · causal representation · 484 papers
Approaches in this cluster:
- Latent-variable causal estimation (117 papers)
Learn structural causal models and deconfounded estimates using generative and latent-variable models. - Causal effect identification (107 papers)
Derive identifiability conditions and estimators for causal effects under latent confounding. - Causal representation identifiability (122 papers)
Prove when causal variables can be recovered from observations under interventions or weak supervision. - Counterfactual identification and bounds (92 papers)
Characterize and bound counterfactual quantities from observational and experimental data. - Causal approaches to fairness (46 papers)
Apply causal graphs and interventional criteria to define and enforce fairness.
Most cited and most cited since 2024:
- CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models (CVPR 2021 · 248 citations)
- Learning Representations for Counterfactual Inference (ICML 2016 · 185 citations)
- Robust Emotion Recognition in Context Debiasing (CVPR 2024 · 29 citations)
- Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference (CVPR 2025 · 25 citations)
causal discovery · causal structure · graphs · 346 papers
Approaches in this cluster:
- Constraint-based structure learning with latents (90 papers)
Recover causal graphs under latent variables and mixed data using recursive, constraint-based algorithms. - Identifiability with hidden confounders (85 papers)
Prove and exploit conditions for discovering causal structure under latent confounding and measurement error. - Interventional causal discovery (68 papers)
Use interventional data and targeting strategies to learn causal graphs efficiently. - Differentiable and score-based DAG learning (52 papers)
Learn causal DAGs by continuous optimization, reinforcement learning, or noise-model assumptions. - Causal discovery from time series (51 papers)
Recover causal structure from temporal data under non-stationarity, latent confounding and Granger-style assumptions.
Most cited and most cited since 2024:
- Discovering Causal Signals in Images (CVPR 2017 · 206 citations)
- Permutation-based Causal Inference Algorithms with Interventions (NeurIPS 2017 · 56 citations)
- Causal-learn: Causal Discovery in Python (NeurIPS 2024 · 26 citations)
- Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model (AAAI 2024 · 8 citations)
Related topics in Probabilistic and scientific modeling
- Dynamical systems (969)
- Uncertainty and Gaussian processes (1,162)
- Molecules and proteins (1,990)
- Variational and generative sampling (2,060)
- Treatment effects (421)
- Differential equations and PDEs (614)
- Bayesian optimization (397)