Systematic Evaluation of Causal Discovery in Visual Model Based\n Reinforcement Learning
Bibliographic record
Abstract
Inducing causal relationships from observations is a classic problem in\nmachine learning. Most work in causality starts from the premise that the\ncausal variables themselves are observed. However, for AI agents such as robots\ntrying to make sense of their environment, the only observables are low-level\nvariables like pixels in images. To generalize well, an agent must induce\nhigh-level variables, particularly those which are causal or are affected by\ncausal variables. A central goal for AI and causality is thus the joint\ndiscovery of abstract representations and causal structure. However, we note\nthat existing environments for studying causal induction are poorly suited for\nthis objective because they have complicated task-specific causal graphs which\nare impossible to manipulate parametrically (e.g., number of nodes, sparsity,\ncausal chain length, etc.). In this work, our goal is to facilitate research in\nlearning representations of high-level variables as well as causal structures\namong them. In order to systematically probe the ability of methods to identify\nthese variables and structures, we design a suite of benchmarking RL\nenvironments. We evaluate various representation learning algorithms from the\nliterature and find that explicitly incorporating structure and modularity in\nmodels can help causal induction in model-based reinforcement learning.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".