Causal inference
Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Causal Inference | 465 | 53.57% |
| Causal Discovery | 34 | 3.92% |
| Decision Making | 27 | 3.11% |
| BIG-bench Machine Learning | 22 | 2.53% |
| Time Series Analysis | 21 | 2.42% |
| Fairness | 19 | 2.19% |
| Selection bias | 16 | 1.84% |
| Recommendation Systems | 12 | 1.38% |
| Reinforcement Learning (RL) | 10 | 1.15% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
