dame-flame: A Python Library Providing Fast Interpretable Matching for
Causal Inference
- URL: http://arxiv.org/abs/2101.01867v3
- Date: Sun, 2 Apr 2023 18:16:37 GMT
- Title: dame-flame: A Python Library Providing Fast Interpretable Matching for
Causal Inference
- Authors: Neha R. Gupta (1), Vittorio Orlandi (1), Chia-Rui Chang (2), Tianyu
Wang (3), Marco Morucci (4), Pritam Dey (1), Thomas J. Howell (1), Xian Sun
(1), Angikar Ghosal (1), Sudeepa Roy (1), Cynthia Rudin (1), Alexander
Volfovsky (1) ((1) Duke University, (2) Harvard University, (3) Fudan
University, (4) New York University)
- Abstract summary: dame-flame is a Python package for performing matching for observational causal inference.
It implements the Dynamic Almost Matching Exactly (DAME) and Fast Large-Scale Almost Matching Exactly (FLAME) algorithms.
- Score: 41.065018571282465
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: dame-flame is a Python package for performing matching for observational
causal inference on datasets containing discrete covariates. This package
implements the Dynamic Almost Matching Exactly (DAME) and Fast Large-Scale
Almost Matching Exactly (FLAME) algorithms, which match treatment and control
units on subsets of the covariates. The resulting matched groups are
interpretable, because the matches are made on covariates, and high-quality,
because machine learning is used to determine which covariates are important to
match on. DAME solves an optimization problem that matches units on as many
covariates as possible, prioritizing matches on important covariates. FLAME
approximates the solution found by DAME via a much faster backward feature
selection procedure. The package provides several adjustable parameters to
adapt the algorithms to specific applications, and can calculate treatment
effect estimates after matching. Descriptions of these parameters, details on
estimating treatment effects, and further examples, can be found in the
documentation at
https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/
Related papers
- Efficient Solvers for SLOPE in R, Python, Julia, and C++ [5.542449901887863]
We present a suite of packages that efficiently solve the Sorted L-One Penalized Estimation problem.<n>The packages feature a highly efficient hybrid coordinate descent algorithm that fits generalized linear models.<n>Our implementation is designed to be fast, memory-efficient, and flexible.
arXiv Detail & Related papers (2025-11-04T10:03:15Z) - Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects [18.60804431844023]
Estimating treatment effects from observational data is challenging due to hidden confounding.
A common framework to address both hidden confounding and selection bias is missing.
arXiv Detail & Related papers (2025-04-29T09:33:56Z) - Semiparametric conformal prediction [79.6147286161434]
Risk-sensitive applications require well-calibrated prediction sets over multiple, potentially correlated target variables.
We treat the scores as random vectors and aim to construct the prediction set accounting for their joint correlation structure.
We report 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) - PyBADS: Fast and robust black-box optimization in Python [11.4219428942199]
PyBADS is an implementation of the Adaptive Direct Search (BADS) algorithm for fast and robust black-box optimization.
It comes along with an easy-to-use Python interface for running the algorithm for running the results.
arXiv Detail & Related papers (2023-06-27T15:54:44Z) - Efficient Approximate Kernel Based Spike Sequence Classification [56.2938724367661]
Machine learning models, such as SVM, require a definition of distance/similarity between pairs of sequences.
Exact methods yield better classification performance, but they pose high computational costs.
We propose a series of ways to improve the performance of the approximate kernel in order to enhance its predictive performance.
arXiv Detail & Related papers (2022-09-11T22:44:19Z) - Parallel Instance Filtering for Malware Detection [0.0]
This work presents a new parallel instance selection algorithm called Parallel Instance Filtering (PIF)
The main idea of the algorithm is to split the data set into non-overlapping subsets of instances covering the whole data set and apply a filtering process for each subset.
We compare the PIF algorithm with several state-of-the-art instance selection algorithms on a large data set of 500,000 malicious and benign samples.
arXiv Detail & Related papers (2022-06-28T11:14:20Z) - Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching [16.899237833310064]
We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline.
The statistics are designed to give smaller values to corrupted matches and than uncorrupted matches.
We demonstrate the efficacy of this method on synthetic and real structure from motion datasets and show that it achieves state-of-the-art accuracy and speed in these tasks.
arXiv Detail & Related papers (2022-01-13T05:33:18Z) - Deep Probabilistic Graph Matching [72.6690550634166]
We propose a deep learning-based graph matching framework that works for the original QAP without compromising on the matching constraints.
The proposed method is evaluated on three popularly tested benchmarks (Pascal VOC, Willow Object and SPair-71k) and it outperforms all previous state-of-the-arts on all benchmarks.
arXiv Detail & Related papers (2022-01-05T13:37:27Z) - Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers [59.06169363181417]
Predict then Interpolate (PI) is an algorithm for learning correlations that are stable across environments.
We prove that by interpolating the distributions of the correct predictions and the wrong predictions, we can uncover an oracle distribution where the unstable correlation vanishes.
arXiv Detail & Related papers (2021-05-26T15:37:48Z) - Ranking a set of objects: a graph based least-square approach [70.7866286425868]
We consider the problem of ranking $N$ objects starting from a set of noisy pairwise comparisons provided by a crowd of equal workers.
We propose a class of non-adaptive ranking algorithms that rely on a least-squares intrinsic optimization criterion for the estimation of qualities.
arXiv Detail & Related papers (2020-02-26T16:19:09Z) - MALTS: Matching After Learning to Stretch [86.84454964051014]
We learn an interpretable distance metric for matching, which leads to substantially higher quality matches.
Our ability to learn flexible distance metrics leads to matches that are interpretable and useful for the estimation of conditional average treatment effects.
arXiv Detail & Related papers (2018-11-18T22:29:59Z)
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.