From Temporal to Contemporaneous Iterative Causal Discovery in the
Presence of Latent Confounders
- URL: http://arxiv.org/abs/2306.00624v1
- Date: Thu, 1 Jun 2023 12:46:06 GMT
- Title: From Temporal to Contemporaneous Iterative Causal Discovery in the
Presence of Latent Confounders
- Authors: Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik
- Abstract summary: We present a constraint-based algorithm for learning causal structures from observational time-series data.
We assume a discrete-time, stationary structural vector autoregressive process, with both temporal and contemporaneous causal relations.
The presented algorithm gradually refines a causal graph by learning long-term temporal relations before short-term ones, where contemporaneous relations are learned last.
- Score: 6.365889364810238
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a constraint-based algorithm for learning causal structures from
observational time-series data, in the presence of latent confounders. We
assume a discrete-time, stationary structural vector autoregressive process,
with both temporal and contemporaneous causal relations. One may ask if
temporal and contemporaneous relations should be treated differently. The
presented algorithm gradually refines a causal graph by learning long-term
temporal relations before short-term ones, where contemporaneous relations are
learned last. This ordering of causal relations to be learnt leads to a
reduction in the required number of statistical tests. We validate this
reduction empirically and demonstrate that it leads to higher accuracy for
synthetic data and more plausible causal graphs for real-world data compared to
state-of-the-art algorithms.
Related papers
- Dynamic Causal Structure Discovery and Causal Effect Estimation [5.943525863330208]
We develop a new framework to model the dynamic causal graph where the causal relations are allowed to be time-varying.
We propose an algorithm that could provide both past-time estimates and future-time predictions on the causal graphs.
arXiv Detail & Related papers (2025-01-11T12:52:39Z) - Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks [12.784885649573994]
Causal discovery from time-series data aims to capture both intra-slice (contemporaneous) and inter-slice (time-lagged) causality.
We propose a novel gradient-based causal discovery approach STIC, which focuses on textbfShort-textbfTerm textbfInvariance using textbfConvolutional neural networks.
arXiv Detail & Related papers (2024-08-15T08:43:28Z) - On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
Temporally causal representation learning aims to identify the latent causal process from time series observations.
Most methods require the assumption that the latent causal processes do not have instantaneous relations.
We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
arXiv Detail & Related papers (2024-05-24T08:08:05Z) - TC-GAT: Graph Attention Network for Temporal Causality Discovery [6.974417592057705]
Causality is frequently intertwined with temporal elements, as the progression from cause to effect is not instantaneous but rather ensconced in a temporal dimension.
We propose a method for extracting causality from the text that integrates both temporal and causal relations.
We present a novel model, TC-GAT, which employs a graph attention mechanism to assign weights to the temporal relationships and leverages a causal knowledge graph to determine the adjacency matrix.
arXiv Detail & Related papers (2023-04-21T02:26:42Z) - DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena [59.291745595756346]
We propose a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay.
Specifically, we leverage a well-established method, logic-based causality, to enable analysts to test the significance of potential causes.
Since an effect can be a cause of other effects, we allow users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram.
arXiv Detail & Related papers (2023-03-12T03:40:21Z) - An Empirical Study: Extensive Deep Temporal Point Process [61.14164208094238]
We first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process.
We propose a Granger causality discovery framework for exploiting the relations among multi-types of events.
arXiv Detail & Related papers (2021-10-19T10:15:00Z) - Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs [49.6661602019124]
We study a spectrum of models derived by generalizing the current state of the art for few-shot link prediction.
We find that a simple zero-shot baseline - which ignores any relation-specific information - achieves surprisingly strong performance.
Experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information.
arXiv Detail & Related papers (2021-02-05T21:04:31Z) - One-shot Learning for Temporal Knowledge Graphs [49.41854171118697]
We propose a one-shot learning framework for link prediction in temporal knowledge graphs.
Our proposed method employs a self-attention mechanism to effectively encode temporal interactions between entities.
Our experiments show that the proposed algorithm outperforms the state of the art baselines for two well-studied benchmarks.
arXiv Detail & Related papers (2020-10-23T03:24:44Z) - Amortized Causal Discovery: Learning to Infer Causal Graphs from
Time-Series Data [63.15776078733762]
We propose Amortized Causal Discovery, a novel framework to learn to infer causal relations from time-series data.
We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance.
arXiv Detail & Related papers (2020-06-18T19:59:12Z) - Inferring Individual Level Causal Models from Graph-based Relational
Time Series [3.332377849866735]
We formalize the problem of causal inference over graph-based relational time-series data.
We propose causal inference models that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes.
arXiv Detail & Related papers (2020-01-16T18:48:40Z)
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.