An Algorithm and Complexity Results for Causal Unit Selection

• URL: http://arxiv.org/abs/2302.14412v1
• Date: Tue, 28 Feb 2023 08:46:51 GMT
• Title: An Algorithm and Complexity Results for Causal Unit Selection
• Authors: Haiying Huang and Adnan Darwiche
• Abstract summary: Unit selection problem aims to identify objects, called units, that are most likely to exhibit a desired mode of behavior when subjected to stimuli. We propose the first exact algorithm for finding optimal units given a broad class of causal objective functions and a fully specified structural causal model.
• Score: 16.307996243413967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The unit selection problem aims to identify objects, called units, that are most likely to exhibit a desired mode of behavior when subjected to stimuli (e.g., customers who are about to churn but would change their mind if encouraged). Unit selection with counterfactual objective functions was introduced relatively recently with existing work focusing on bounding a specific class of objective functions, called the benefit functions, based on observational and interventional data -- assuming a fully specified model is not available to evaluate these functions. We complement this line of work by proposing the first exact algorithm for finding optimal units given a broad class of causal objective functions and a fully specified structural causal model (SCM). We show that unit selection under this class of objective functions is $\text{NP}^\text{PP}$-complete but is $\text{NP}$-complete when unit variables correspond to all exogenous variables in the SCM. We also provide treewidth-based complexity bounds on our proposed algorithm while relating it to a well-known algorithm for Maximum a Posteriori (MAP) inference.

Related papers

• Causal Unit Selection using Tractable Arithmetic Circuits [12.223629720083148]
  We introduce a new approach for unit selection that is not necessarily limited by the constrained treewidth. This is done through compiling the meta-model into a special class of tractable arithmetic circuits.
  arXiv  Detail & Related papers  (2024-04-10T02:02:34Z)
• Multi-objective Binary Coordinate Search for Feature Selection [0.24578723416255746]
  We propose the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems. Results indicate the significant superiority of our method over NSGA-II, on five real-world large-scale datasets.
  arXiv  Detail & Related papers  (2024-02-20T00:50:26Z)
• Efficient Model-Free Exploration in Low-Rank MDPs [76.87340323826945]
  Low-Rank Markov Decision Processes offer a simple, yet expressive framework for RL with function approximation. Existing algorithms are either (1) computationally intractable, or (2) reliant upon restrictive statistical assumptions. We propose the first provably sample-efficient algorithm for exploration in Low-Rank MDPs.
  arXiv  Detail & Related papers  (2023-07-08T15:41:48Z)
• Group Fairness in Non-monotone Submodular Maximization [19.29174615532181]
  We study the non-monotone submodular problem subject to novel group fairness constraints. We develop the first constant-factor approximation algorithms for this problem.
  arXiv  Detail & Related papers  (2023-02-03T04:51:54Z)
• Should All Proposals be Treated Equally in Object Detection? [110.27485090952385]
  The complexity-precision trade-off of an object detector is a critical problem for resource constrained vision tasks. It is hypothesized that improved detection efficiency requires a paradigm shift, towards the unequal processing of proposals. This results in better utilization of available computational budget, enabling higher accuracy for the same FLOPS.
  arXiv  Detail & Related papers  (2022-07-07T18:26:32Z)
• Compactness Score: A Fast Filter Method for Unsupervised Feature Selection [66.84571085643928]
  We propose a fast unsupervised feature selection method, named as, Compactness Score (CSUFS) to select desired features. Our proposed algorithm seems to be more accurate and efficient compared with existing algorithms.
  arXiv  Detail & Related papers  (2022-01-31T13:01:37Z)
• Submodular + Concave [53.208470310734825]
  It has been well established that first order optimization methods can converge to the maximal objective value of concave functions. In this work, we initiate the determinant of the smooth functions convex body $$F(x) = G(x) +C(x)$. This class of functions is an extension of both concave and continuous DR-submodular functions for which no guarantee is known.
  arXiv  Detail & Related papers  (2021-06-09T01:59:55Z)
• CACTUS: Detecting and Resolving Conflicts in Objective Functions [16.784454432715712]
  In multi-objective optimization, conflicting objectives and constraints is a major area of concern. In this paper, we extend this line of work by prototyping a technique to visualize multi-objective objective functions. We show that our technique helps users interactively specify meaningful objective functions by resolving potential conflicts for a classification task.
  arXiv  Detail & Related papers  (2021-03-13T22:38:47Z)
• Linear Classifier Combination via Multiple Potential Functions [0.6091702876917279]
  We propose a novel concept of calculating a scoring function based on the distance of the object from the decision boundary and its distance to the class centroid. An important property is that the proposed score function has the same nature for all linear base classifiers.
  arXiv  Detail & Related papers  (2020-10-02T08:11:51Z)
• From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models [82.95892656532696]
  Submodular functions have been studied extensively in machine learning and data mining. In this work, we propose a continuous DR-submodular extension for integer submodular functions. We formulate a new probabilistic model which is defined through integer submodular functions.
  arXiv  Detail & Related papers  (2020-06-01T22:20:45Z)
• Invariant Feature Coding using Tensor Product Representation [75.62232699377877]
  We prove that the group-invariant feature vector contains sufficient discriminative information when learning a linear classifier. A novel feature model that explicitly consider group action is proposed for principal component analysis and k-means clustering.
  arXiv  Detail & Related papers  (2019-06-05T07:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.