Sample Efficient Active Learning of Causal Trees
Authors: Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider several experimental settings and for each setting we simulate 200 random trees of n nodes. Here we present a subset of the results, more are described in Appendix I. We generate an undirected tree with three different strategies: a) sampling uniformly from the space of undirected trees, b) generating power-law trees, and c) generating high degree d = n/2 random graphs and then creating an undirected version of the BFS tree. |
| Researcher Affiliation | Collaboration | Kristjan Greenewald IBM Research MIT-IBM Watson AI Lab kristjan.h.greenewald@ibm.com Dmitriy Katz IBM Research MIT-IBM Watson AI Lab dkatzrog@us.ibm.com Karthikeyan Shanmugam IBM Research MIT-IBM Watson AI Lab karthikeyan.shanmugam2@ibm.com Sara Magliacane IBM Research MIT-IBM Watson AI Lab sara.magliacane@ibm.com Murat Kocaoglu IBM Research MIT-IBM Watson AI Lab murat@ibm.com Enric Boix-Adser a MIT MIT-IBM Watson AI Lab eboix@mit.edu Guy Bresler MIT MIT-IBM Watson AI Lab guy@mit.edu |
| Pseudocode | Yes | Algorithm 1 Central Node Algorithm input Observational tree G0. Confidence parameter δ. 1: t 0. 2: q0(i) 1 n, 8i = 1, . . . , n. 3: while maxi qt(i) 1 δ do 4: t t + 1. 5: Identify central node index vc(t) of G with respect to qt 1 (Algorithm 4). 6: Intervene on node vc(t) and observe x1, . . . , xn. 7: Update posterior distribution qt as given in Lemma 1. 8: end while output argmaxi qt(i) as the estimated root node of G0. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | We consider several experimental settings and for each setting we simulate 200 random trees of n nodes. We generate an undirected tree with three different strategies: a) sampling uniformly from the space of undirected trees, b) generating power-law trees, and c) generating high degree d = n/2 random graphs and then creating an undirected version of the BFS tree. Once we have a tree, we pick the root node uniformly at random. In this section we focus on binary random variables, where each variable is a function of its parent: if XP ai = 0, then Xi Bern ( ), else Xi Bern (1 ), where for each variable we sample uniformly from [δ, 0.5 δ]. The root node is distributed as Xr Bern (0.5). We show similar results with discrete variables in Appendix I. |
| Dataset Splits | No | The paper describes generating random trees and performing simulations but does not specify any train/validation/test dataset splits, cross-validation, or random seeds for data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. It only mentions 'simulated experiments'. |
| Software Dependencies | No | The paper mentions 'simulated experiments' but does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | We consider several experimental settings and for each setting we simulate 200 random trees of n nodes. ... We generate an undirected tree with three different strategies: a) sampling uniformly from the space of undirected trees, b) generating power-law trees, and c) generating high degree d = n/2 random graphs and then creating an undirected version of the BFS tree. Once we have a tree, we pick the root node uniformly at random. In this section we focus on binary random variables, where each variable is a function of its parent: if XP ai = 0, then Xi Bern ( ), else Xi Bern (1 ), where for each variable we sample uniformly from [δ, 0.5 δ]. The root node is distributed as Xr Bern (0.5). |