Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Do Not Marginalize Mechanisms, Rather Consolidate!
Authors: Moritz Willig, Matej Zečević, Devendra Dhami, Kristian Kersting
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate consolidation on two examples. First, obtaining a causal model of reduced size and, secondly, revealing the underlying policy of a causal decision making process. ... Figure 4 shows the resulting plot of the consolidated model under interventions do(S12 = 1) and do(S24 = 1). We successfully demonstrated the power of consolidation models for dynamical system while preserving the ability to intervene. In our second example we apply consolidation to a more complex causal graph relating the game state of a simple platformer environment to the actions of an agent. |
| Researcher Affiliation | Academia | Moritz Willig Technical University of Darmstadt EMAIL Matej Zeˇcevi c Technical University of Darmstadt EMAIL Devendra Singh Dhami Eindhoven University of Technology Hessian Center for AI (hessian.AI) EMAIL Kristian Kersting Technical University Darmstadt Hessian Center for AI (hessian.AI) German Research Center for AI (DFKI) EMAIL |
| Pseudocode | Yes | Figure 3: CONSOLIDATE Algorithm. The above pseudo-code summarizes the consolidation algorithm as described in this paper by utilizing causal compositional variables and partitioned SCM to obtain simplified SCM. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | No | The paper describes examples like 'time series data' and 'game agent policy' with their SCMs, but does not provide concrete access information (links, DOIs, specific citations with authors/year) for publicly available datasets used for training or evaluation. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We observe the tool to loose some percentage of its sharpness per day depending on its utilization Ut. The intervention do(St = 1) resets the sharpness to a certain sharpness. ... U = {Ut = N(0.5, 0.05^2)} V = {Lt, St, At} I = P({do(St = 1), do(Lt = 1.0), do(St = 1, Lt = 1)}) fl(l, u) := (1.0 - 0.002u)l fst(st - 1, u) := (1.0 - 0.3u)st - 1 fa(s) := 0.8s^2 |