Applications of statistical causal inference in software engineering

• URL: http://arxiv.org/abs/2211.11482v3
• Date: Thu, 23 Mar 2023 07:25:51 GMT
• Title: Applications of statistical causal inference in software engineering
• Authors: Julien Siebert
• Abstract summary: This paper reviews existing work in software engineering that applies statistical causal inference methods. Our results show that the application of statistical causal inference methods is relatively recent and that the corresponding research community remains relatively fragmented.
• Score: 2.969705152497174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper reviews existing work in software engineering that applies statistical causal inference methods. These methods aim at estimating causal effects from observational data. The review covers 32 papers published between 2010 and 2022. Our results show that the application of statistical causal inference methods is relatively recent and that the corresponding research community remains relatively fragmented.

Related papers

• A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineering [5.687882380471718]
  Concerns about the correct application of empirical methodologies have existed since the 2006 Dagstuhl seminar on Empirical Software Engineering. We conducted a literature survey of 27,000 empirical studies, using LLMs to classify statistical methodologies as adequate or inadequate. We selected 30 primary studies and held a workshop with 33 ESE experts to assess their ability to identify and resolve statistical issues.
  arXiv  Detail & Related papers  (2025-01-22T09:05:01Z)
• A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment [4.649794383775257]
  We reanalyze the only known controlled experiment investigating the impact of passive voice on the subsequent activity of domain modeling. Our results reveal that the effects observed by the original authors turned out to be much less significant than previously assumed.
  arXiv  Detail & Related papers  (2024-02-16T16:24:00Z)
• Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation [137.3520153445413]
  A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
  arXiv  Detail & Related papers  (2023-07-11T02:58:10Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions [10.51554436183424]
  This paper discusses some novel techniques based on structural causal models. We apply these ideas to analyzing public data about programmer performance in Code Jam. We find considerable differences between a purely associational and a causal analysis of the very same data.
  arXiv  Detail & Related papers  (2023-01-18T13:46:16Z)
• CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data [61.088705993848606]
  We introduce CausalBench, a benchmark suite for evaluating causal inference methods on real-world interventional data. CaulBench incorporates biologically-motivated performance metrics, including new distribution-based interventional metrics.
  arXiv  Detail & Related papers  (2022-10-31T13:04:07Z)
• An evaluation framework for comparing causal inference models [3.1372269816123994]
  We use the proposed evaluation methodology to compare several state-of-the-art causal effect estimation models. The main motivation behind this approach is the elimination of the influence of a small number of instances or simulation on the benchmarking process.
  arXiv  Detail & Related papers  (2022-08-31T21:04:20Z)
• Valid Inference After Causal Discovery [73.87055989355737]
  We develop tools for valid post-causal-discovery inference. We show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates.
  arXiv  Detail & Related papers  (2022-08-11T17:40:45Z)
• Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning [90.59143158534849]
  The recent emergence of reinforcement learning has created a demand for robust statistical inference methods. Existing methods for statistical inference in online learning are restricted to settings involving independently sampled observations. The online bootstrap is a flexible and efficient approach for statistical inference in linear approximation algorithms, but its efficacy in settings involving Markov noise has yet to be explored.
  arXiv  Detail & Related papers  (2021-08-08T18:26:35Z)
• A Survey on Causal Inference [64.45536158710014]
  Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics. Various causal effect estimation methods for observational data have sprung up.
  arXiv  Detail & Related papers  (2020-02-05T21:35:29Z)

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.