ArticlePloS one2016
Limits to Causal Inference with State-Space Reconstruction for Infectious Disease.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Sensitivity of Convergent Cross Mapping to temporal discontinuities: A case study of seasonal influenza in Hong Kong.Infectious Disease Modelling · 2026Article
- The pitfalls of incidence-based time series regression for inferring the effects of weather on infectious diseases.PLoS computational biology · 2026Article
- The limitations of non-mechanistic methods for characterizing pathogen-pathogen interactions: A simulation study.PLoS computational biology · 2026Article
- Environmental drivers of influenza A and B: causal analysis and forecasting.BMC public health · 2026Article
- Unified climate factors predict influenza outbreak seasonality across tropical and temperate regions.PNAS nexus · 2026Article
- Susceptible host dynamics explain pathogen resilience to perturbations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Susceptible host dynamics explain pathogen resilience to perturbations.bioRxiv : the preprint server for biology · 2025Article
- The Actual Clinical Situation Ruthlessly Exposes the Challenge of Rational Care for Nosocomial and Community-Acquired Infections and Requires Even More Efforts for Satisfactory Antibiotic Stewardship.Antibiotics (Basel, Switzerland) · 2025Article
- The impact of data resolution on dynamic causal inference in multiscale ecological networks.Communications biology · 2024Article
- Decomposing causality into its synergistic, unique, and redundant components.Nature communications · 2024Article
- Multifaceted effects of variable biotic interactions on population stability in complex interaction webs.Communications biology · 2024Article
- A rigorous and versatile statistical test for correlations between stationary time series.PLoS biology · 2024Article
- Causal inference from cross-sectional earth system data with geographical convergent cross mapping.Nature communications · 2023Article
- A general model-based causal inference method overcomes the curse of synchrony and indirect effect.Nature communications · 2023Article
- Inferring a Causal Relationship between Environmental Factors and Respiratory Infections Using Convergent Cross-Mapping.Entropy (Basel, Switzerland) · 2023Article
- Loss of Earth system resilience during early Eocene transient global warming events.Science advances · 2023Article
- Decomposing predictability to identify dominant causal drivers in complex ecosystems.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Review
- Inferring causal relations from observational long-term carbon and water fluxes records.Scientific reports · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious diseases are notorious for their complex dynamics, which make it difficult to fit models to test hypotheses. Methods based on state-space reconstruction have been proposed to infer causal interactions in noisy, nonlinear dynamical systems. These "model-free" methods are collectively known as convergent cross-mapping (CCM). Although CCM has theoretical support, natural systems routinely violate its assumptions. To identify the practical limits of causal inference under CCM, we simulated the dynamics of two pathogen strains with varying interaction strengths. The original method of CCM is extremely sensitive to periodic fluctuations, inferring interactions between independent strains that oscillate with similar frequencies. This sensitivity vanishes with alternative criteria for inferring causality. However, CCM remains sensitive to high levels of process noise and changes to the deterministic attractor. This sensitivity is problematic because it remains challenging to gauge noise and dynamical changes in natural systems, including the quality of reconstructed attractors that underlie cross-mapping. We illustrate these challenges by analyzing time series of reportable childhood infections in New York City and Chicago during the pre-vaccine era. We comment on the statistical and conceptual challenges that currently limit the use of state-space reconstruction in causal inference.
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