# Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution

arXiv: https://arxiv.org/abs/1801.04016 · HTML: https://mlsanity.com/paper/1801.04016/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2018.

## Built on by (most cited first, 30 of 66)

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- [Passive learning of active causal strategies in agents and language models](https://mlsanity.com/paper/2305.16183/) (2023)
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- [Causal models in string diagrams](https://mlsanity.com/paper/2304.07638/) (2023)
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- [Interpretation of Black Box NLP Models: A Survey](https://mlsanity.com/paper/2203.17081/) (2022)
- [Causalainer: Causal Explainer for Automatic Video Summarization](https://mlsanity.com/paper/2305.00455/) (2023)
- [Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation](https://mlsanity.com/paper/2102.06573/) (2021)
- [A Survey on Open-Set Image Recognition](https://mlsanity.com/paper/2312.15571/) (2023)
- [Deep Learning With DAGs](https://mlsanity.com/paper/2401.06864/) (2024)
- [Intelligent Computing: The Latest Advances, Challenges and Future](https://mlsanity.com/paper/2211.11281/) (2022)
- [Conditional Modeling Based Automatic Video Summarization](https://mlsanity.com/paper/2311.12159/) (2023)
- [Invariant Ancestry Search](https://mlsanity.com/paper/2202.00913/) (2022)
- [DiConStruct: Causal Concept-based Explanations through Black-Box Distillation](https://mlsanity.com/paper/2401.08534/) (2024)

## Abstract (arXiv)

Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.