A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
- URL: http://arxiv.org/abs/2303.15027v4
- Date: Tue, 12 Mar 2024 20:14:45 GMT
- Title: A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
- Authors: Uzma Hasan, Emam Hossain, Md Osman Gani
- Abstract summary: Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data.
We present an extensive discussion on the methods designed to perform causal discovery from both independent and identically distributed (I.I.D.) data and time series data.
- Score: 4.57769506869942
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The ability to understand causality from data is one of the major milestones
of human-level intelligence. Causal Discovery (CD) algorithms can identify the
cause-effect relationships among the variables of a system from related
observational data with certain assumptions. Over the years, several methods
have been developed primarily based on the statistical properties of data to
uncover the underlying causal mechanism. In this study, we present an extensive
discussion on the methods designed to perform causal discovery from both
independent and identically distributed (I.I.D.) data and time series data. For
this purpose, we first introduce the common terminologies used in causal
discovery literature and then provide a comprehensive discussion of the
algorithms designed to identify causal relations in different settings. We
further discuss some of the benchmark datasets available for evaluating the
algorithmic performance, off-the-shelf tools or software packages to perform
causal discovery readily, and the common metrics used to evaluate these
methods. We also evaluate some widely used causal discovery algorithms on
multiple benchmark datasets and compare their performances. Finally, we
conclude by discussing the research challenges and the applications of causal
discovery algorithms in multiple areas of interest.
Related papers
- CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series [4.008958683836471]
CAnDOIT is a causal discovery method to reconstruct causal models using both observational and interventional data.
The use of interventional data in the causal analysis is crucial for real-world applications, such as robotics.
A Python implementation of CAnDOIT has also been developed and is publicly available on GitHub.
arXiv Detail & Related papers (2024-10-03T13:57:08Z) - A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time
Series [0.8252665500568257]
Causal inference is a fundamental research topic for discovering the cause-effect relationships in many disciplines.
Some approaches may only be able to identify linear relationships, while others are applicable for non-linearities.
This publication proposes a novel data-driven multi-split causal ensemble model to combine the strengths of different causality base algorithms.
arXiv Detail & Related papers (2024-03-04T14:20:41Z) - Causal Discovery from Temporal Data: An Overview and New Perspectives [6.251443497694126]
Analyzing temporal data is extremely valuable for various applications.
causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task.
In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions.
arXiv Detail & Related papers (2023-03-17T16:45:01Z) - 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) - 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) - Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data.
We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism.
We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
arXiv Detail & Related papers (2022-06-30T06:00:13Z) - Learning Neural Causal Models with Active Interventions [83.44636110899742]
We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process.
Our method significantly reduces the required number of interactions compared with random intervention targeting.
We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
arXiv Detail & Related papers (2021-09-06T13:10:37Z) - Consistency of mechanistic causal discovery in continuous-time using
Neural ODEs [85.7910042199734]
We consider causal discovery in continuous-time for the study of dynamical systems.
We propose a causal discovery algorithm based on penalized Neural ODEs.
arXiv Detail & Related papers (2021-05-06T08:48:02Z) - On the Sample Complexity of Causal Discovery and the Value of Domain
Expertise [0.0]
Causal discovery methods seek to identify causal relations between random variables from purely observational data.
In this paper, we analyze the sample complexity of causal discovery algorithms without a CI oracle.
Our methods allow us to quantify the value of domain expertise in terms of data samples.
arXiv Detail & Related papers (2021-02-05T16:26:17Z) - 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.