Research map / Probabilistic and scientific modeling / Causal discovery
Causal effect identification
Derive identifiability conditions and estimators for causal effects under latent confounding.
107 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Causal discovery, keyword cluster “counterfactual · causal inference · causal representation”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
37 papers in 2023–24 and 27 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 15.9% to 8.1%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · Causal Explanation Under Indeterminism: A Sampling Approach (AAAI 2016 · 4 citations)
- 2017 · Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables (ICML 2017 · 10 citations)
- 2018 · Learning Independent Causal Mechanisms (ICML 2018 · 87 citations)
- 2019 · Sensitivity Analysis of Linear Structural Causal Models (ICML 2019 · 26 citations)
- 2020 · How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? (NeurIPS 2020 · 18 citations)
- 2021 · Estimating Identifiable Causal Effects through Double Machine Learning (AAAI 2021 · 41 citations)
- 2022 · Identification of Linear Latent Variable Model with Arbitrary Distribution (AAAI 2022 · 7 citations)
- 2023 · Identifiability Guarantees for Causal Disentanglement from Soft Interventions (NeurIPS 2023 · 9 citations)
- 2024 · Identifiability of Direct Effects from Summary Causal Graphs (AAAI 2024 · 2 citations)
- 2025 · A Practical Approach to Causal Inference over Time (AAAI 2025 · 3 citations)
- 2026 · Evaluating Bivariate Causal Statements Based on Mutual Compatibility (ICML 2026 · 0 citations)
Earliest papers here
- Causal Explanation Under Indeterminism: A Sampling Approach (AAAI 2016 · 4 citations)
- Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables (ICML 2017 · 10 citations)
- Learning Independent Causal Mechanisms (ICML 2018 · 87 citations)
What has been tried
70 papers here report 423 measured changes (a component added, removed or swapped, compared with a baseline): helped 62%, no clear effect 15%, hurt 15%, mixed 8%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| match learned representation dimension to ground-truth CFD dimension 2 rather than set it to 1 | Ground-truth CFD dimension 2, N=20k · estimation bias (%) | 9.88 ± 1.36 -> 4.16 ± 8.90 | Helped | Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder (ICLR 2024 · 1 citation) |
| Use GIM instead of BaCaDI* (non-amortized point estimate) | Synthetic nonlinear SCMs · causal structure accuracy | Helped | Generative Intervention Models for Causal Perturbation Modeling (ICML 2025 · 0 citations) | |
| non-bijective noise transform (segmented linear, interval 0.2) added to Triangle_NLIN SCM | Triangle_NLIN SCM · RMSE counterfactual | 0.132 vs 0.127 baseline | No clear effect | Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption (ICML 2025 · 0 citations) |
| Extend pre-training to graph sizes up to 60 nodes | Synthetic graphs with 21–50 nodes · MSE | Helped | Do-PFN: In-Context Learning for Causal Effect Estimation (NeurIPS 2025 · 0 citations) | |
| add the spectral loss | NorESM2 pre-industrial, GMST · LSD | 0.658 -> 0.444 | Helped | Causal Climate Emulation with Bayesian Filtering (NeurIPS 2025 · 0 citations) |
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 64 papers; from OpenAlex and the papers' affiliations).
- Purdue University · 6 papers
- Massachusetts Institute of Technology · 5 papers
- University of Cambridge · 4 papers
- Max Planck Institute for Intelligent Systems · 4 papers
- Columbia University · 3 papers
- EPFL · 3 papers
- Swiss Federal Institute of Technology Lausanne · 3 papers
- Technische Universität München · 3 papers
- Stanford University · 3 papers
- University of California, San Diego · 3 papers
Use it
Download BibTeX of 40 key papersOpen the topic in the interactive map
Get alerts. Describe your idea in scime atlas and save it (free scime account): you get a weekly email when new preprints land close to it.
Related approaches
Same keyword cluster
- Latent-variable causal estimation (117 papers)
- Causal representation identifiability (122 papers)
- Counterfactual identification and bounds (92 papers)
- Causal approaches to fairness (46 papers)
Elsewhere in the topic
- Constraint-based structure learning with latents (90 papers)
- Identifiability with hidden confounders (85 papers)
- Interventional causal discovery (68 papers)
- Differentiable and score-based DAG learning (52 papers)
- Causal discovery from time series (51 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).