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Navigating Time's Possibilities: Plausible Counterfactual Explanations for Multivariate Time-Series Forecast through Genetic Algorithms

Gianlucca Zuin, Adriano Veloso

TL;DR

A novel method for counterfactual learning in the context of multivariate time series analysis and forecast is presented, employing Granger causality to enhance the reliability of identified causal relationships, rigorously assessing their statistical significance.

Abstract

Counterfactual learning has become promising for understanding and modeling causality in complex and dynamic systems. This paper presents a novel method for counterfactual learning in the context of multivariate time series analysis and forecast. The primary objective is to uncover hidden causal relationships and identify potential interventions to achieve desired outcomes. The proposed methodology integrates genetic algorithms and rigorous causality tests to infer and validate counterfactual dependencies within temporal sequences. More specifically, we employ Granger causality to enhance the reliability of identified causal relationships, rigorously assessing their statistical significance. Then, genetic algorithms, in conjunction with quantile regression, are used to exploit these intricate causal relationships to project future scenarios. The synergy between genetic algorithms and causality tests ensures a thorough exploration of the temporal dynamics present in the data, revealing hidden dependencies and enabling the projection of outcomes under hypothetical interventions. We evaluate the performance of our algorithm on real-world data, showcasing its ability to handle complex causal relationships, revealing meaningful counterfactual insights, and allowing for the prediction of outcomes under hypothetical interventions.

Navigating Time's Possibilities: Plausible Counterfactual Explanations for Multivariate Time-Series Forecast through Genetic Algorithms

TL;DR

A novel method for counterfactual learning in the context of multivariate time series analysis and forecast is presented, employing Granger causality to enhance the reliability of identified causal relationships, rigorously assessing their statistical significance.

Abstract

Counterfactual learning has become promising for understanding and modeling causality in complex and dynamic systems. This paper presents a novel method for counterfactual learning in the context of multivariate time series analysis and forecast. The primary objective is to uncover hidden causal relationships and identify potential interventions to achieve desired outcomes. The proposed methodology integrates genetic algorithms and rigorous causality tests to infer and validate counterfactual dependencies within temporal sequences. More specifically, we employ Granger causality to enhance the reliability of identified causal relationships, rigorously assessing their statistical significance. Then, genetic algorithms, in conjunction with quantile regression, are used to exploit these intricate causal relationships to project future scenarios. The synergy between genetic algorithms and causality tests ensures a thorough exploration of the temporal dynamics present in the data, revealing hidden dependencies and enabling the projection of outcomes under hypothetical interventions. We evaluate the performance of our algorithm on real-world data, showcasing its ability to handle complex causal relationships, revealing meaningful counterfactual insights, and allowing for the prediction of outcomes under hypothetical interventions.
Paper Structure (6 sections, 7 equations, 6 figures, 1 table)

This paper contains 6 sections, 7 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Approach
  4. Experiments
  5. Discussion
  6. Conclusion

Figures (6)

  • Figure 1: Alkaline Closed Loop vacuum system.
  • Figure 2: Prediction and ground-truth values during cross-validation for a subset of the data.
  • Figure 3: Granger Causality Heatmap. Darker cells are associated with a stronger relationship with statistically significant pairs being marked.
  • Figure 4: Quantile forecasts for an actionable variables and some possible future pathways. Solid lines represent the historical values, while dashed lines illustrate the possible futures. The red dashed line highlights the most likely pathway. Specifically, values in the range [1.84, 1.98] are plausible for 15 seconds (5 steps) into the future.
  • Figure 5: Population fitness during the GA execution projecting a plausible return from a near vacuum break. While searching for a near future where the pressure level reaches 5.0 Pa. The solution found lies in the range between 4.0 Pa and 6.0 Pa, satisfying the constraint of recovering from a vacuum break.
  • ...and 1 more figures