Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

• URL: http://arxiv.org/abs/2003.01805v2
• Date: Sat, 8 Aug 2020 14:05:26 GMT
• Title: Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
• Authors: Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
• Abstract summary: We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions. The regions are found as either the solution to a mixed integer program, or using a (fast) approximation algorithm. The result is an interpretable and tailored estimate of a causal effect for each unit.
• Score: 76.36298211419158
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough that many matches are created for each unit and small enough that the treatment effect is roughly constant throughout. The regions are found as either the solution to a mixed integer program, or using a (fast) approximation algorithm. The result is an interpretable and tailored estimate of a causal effect for each unit.

Related papers

• Semiparametric conformal prediction [79.6147286161434]
  We construct a conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores. We flexibly estimate the joint cumulative distribution function (CDF) of the scores. Our method yields desired coverage and competitive efficiency on a range of real-world regression problems.
  arXiv  Detail & Related papers  (2024-11-04T14:29:02Z)
• Semisupervised score based matching algorithm to evaluate the effect of public health interventions [3.221788913179251]
  In one-to-one matching algorithms, a large number of "pairs" to be matched could mean both the information from a large sample and a large number of tasks. We propose a novel one-to-one matching algorithm based on a quadratic score function $S_beta(x_i,x_j)= betaT (x_i-x_j)(x_i-x_j)T beta$.
  arXiv  Detail & Related papers  (2024-03-19T02:24:16Z)
• Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference [44.644520116360106]
  We estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control. We propose a clustered switchback design, where units are grouped into clusters and time steps are grouped into blocks. We show that for graphs that admit good clustering, a truncated Horvitz-Thompson estimator achieves a $tilde O(1/NT)$ mean squared error (MSE) Our results simultaneously generalize the results from citethu2022switchback,ugander2013graph and citetleung2022rate
  arXiv  Detail & Related papers  (2023-12-25T01:00:58Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• Matching entropy based disparity estimation from light field [0.0]
  A matching window satisfying three characteristics: texture richness, disparity consistency and anti-occlusion should be able to prevent mismatches. We propose matching entropy in the spatial domain of light field to measure the amount of correct information in a matching window. A two-step process can reduce mismatches and redundant calculations by selecting effective matching windows.
  arXiv  Detail & Related papers  (2022-10-28T07:12:00Z)
• An Improved Greedy Algorithm for Subset Selection in Linear Estimation [5.994412766684842]
  We consider a subset selection problem in a spatial field where we seek to find a set of k locations whose observations provide the best estimate of the field value at a finite set of prediction locations. One approach for observation selection is to perform a grid discretization of the space and obtain an approximate solution using the greedy algorithm. We propose a method to reduce the computational complexity by considering a search space consisting only of prediction locations and centroids of cliques formed by the prediction locations.
  arXiv  Detail & Related papers  (2022-03-30T05:52:16Z)
• Local versions of sum-of-norms clustering [77.34726150561087]
  We show that our method can separate arbitrarily close balls in the ball model. We prove a quantitative bound on the error incurred in the clustering of disjoint connected sets.
  arXiv  Detail & Related papers  (2021-09-20T14:45:29Z)
• Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks [57.64866581615309]
  We propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks. Results indicate that Recombination and Recalibration improve the results of a competitive baseline, and generalize across three different problems.
  arXiv  Detail & Related papers  (2020-06-19T15:45:03Z)
• Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation [72.40827239394565]
  We propose to compute features only at sparsely sampled locations. We then densely reconstruct the feature map with an efficient procedure. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
  arXiv  Detail & Related papers  (2020-03-19T15:36:31Z)
• Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference [73.23326654892963]
  We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs.
  arXiv  Detail & Related papers  (2020-03-02T15:21:20Z)
• To Split or Not to Split: The Impact of Disparate Treatment in Classification [8.325775867295814]
  Disparate treatment occurs when a machine learning model yields different decisions for individuals based on a sensitive attribute. We introduce the benefit-of-splitting for quantifying the performance improvement by splitting classifiers. We prove an equivalent expression for the benefit-of-splitting which can be efficiently computed by solving small-scale convex programs.
  arXiv  Detail & Related papers  (2020-02-12T04:05:31Z)

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.