Blockchain Meets Causal AI to Forecast Economies Through Crisis and Chaos

Macroeconomic forecasting has long been one of the most consequential—and most fragile—arts in modern economics. Central banks, finance ministries, and international institutions depend on forecasts of inflation, growth, and unemployment to set interest rates, design budgets, and manage financial risk. Yet the turbulent quarter-century since 2000 has repeatedly humbled even the most sophisticated models. The 2008 global financial crisis, the COVID-19 pandemic, and the 2022 inflationary shock each produced abrupt, sweeping changes in the relationships among economic variables, leaving forecasting systems calibrated on historical data struggling to keep up. A new study published in Frontiers in Blockchain proposes an ambitious answer: a forecasting framework that fuses causal machine learning, explainable artificial intelligence, and a blockchain-based governance layer into a single, transparent architecture designed to survive structural upheaval.

The research, conducted by Oumaima Abouzaid and Faouzi Boussedra of Université Chouaib Doukkali in El Jadida, Morocco, begins from a diagnosis that will resonate with anyone who watched economic predictions collapse during recent crises. Traditional econometric tools—ARIMA models, Vector Autoregressions, Dynamic Stochastic General Equilibrium frameworks—rest on assumptions of parameter stability and largely linear relationships. When structural breaks occur, meaning fundamental shifts in the statistical properties governing economic variables, those assumptions fail. Parameters estimated under one regime become biased in the next, statistical inference weakens, and forecast errors balloon precisely when accurate predictions matter most.

To confront this instability, the authors built their framework around a sequence of complementary techniques. First, Bai–Perron structural break analysis detects endogenous breakpoints in the data without requiring prior assumptions about when changes occur. Second, Markov-Switching models identify latent economic regimes—expansionary and recessionary states with distinct statistical properties—whose labels are then fed into the forecasting engine as additional state-dependent features. Only after this regime-aware preparation do the machine learning models take over, trained on data that explicitly encode the economy’s shifting character rather than pretending it never changes.

The centerpiece of the predictive architecture is Double Machine Learning, or DML, a causal inference framework that integrates machine learning algorithms into econometric estimation while preserving valid statistical inference in high-dimensional settings. DML relies on orthogonalization and sample-splitting procedures to estimate causal effects while controlling for large numbers of confounding variables, reducing sensitivity to overfitting and omitted variable bias. Because macroeconomic time series exhibit serial dependence and volatility clustering, the authors adapted the standard approach by using block cross-fitting—partitioning observations into contiguous time blocks rather than random splits—and by computing heteroskedasticity and autocorrelation consistent standard errors with Newey–West corrections. Alongside DML, the framework employs Causal Forests, which extend Random Forest methods to estimate heterogeneous treatment effects across different economic regimes, recognizing that the same policy variable may exert very different influences in calm periods than in crises.

The empirical analysis drew on a monthly macroeconomic dataset spanning January 2000 to March 2025, yielding 303 observations per variable. The dataset combined indicators from the Federal Reserve Economic Data service, the International Monetary Fund, the OECD, the World Bank, and the Economic Policy Uncertainty database. The target variable was inflation, measured as year-over-year Consumer Price Index growth, with explanatory variables including GDP growth, unemployment, the federal funds rate, the dollar exchange rate index, Brent oil prices, M2 money supply growth, the VIX volatility index, and the Economic Policy Uncertainty index. Missing observations, which made up less than 2.3 percent of the data, were imputed using Kalman smoothing, and extreme outliers were winsorized at the first and ninety-ninth percentiles. The data were split chronologically into training, validation, and test subsets to avoid look-ahead bias, with rolling-window cross-validation used for tuning.

The results were striking. Across root mean squared error, mean absolute error, and mean absolute percentage error, the DML framework outperformed every benchmark, including conventional Vector Autoregressions, time-varying parameter VARs, Random Forests, XGBoost, and Long Short-Term Memory neural networks. The DML model achieved an RMSE of 1.01, compared with 1.58 for the standard VAR and 1.12 for the LSTM network. The gap widened during crises. Averaged across the 2008 financial crisis, the COVID-19 shock, and the 2022 inflation surge, the DML framework posted an RMSE of 1.49, against 2.55 for the VAR and 1.70 for the LSTM. The pandemic period proved the hardest test for all models, reflecting unprecedented volatility and nonlinear disruption, yet the causal framework remained the most resilient throughout. Robustness checks with alternative data splits and samples excluding individual crisis episodes confirmed that the results were not artifacts of any particular configuration.

Beyond raw predictive accuracy, the causal estimates offered economically meaningful insights into what drives inflation. Oil prices exerted the strongest positive causal effect, at 0.52, underscoring the dominance of energy price transmission during supply-side shocks. Money supply growth followed at 0.41, suggesting that expansionary liquidity conditions amplify inflationary pressure, while exchange rate depreciation contributed 0.31 through imported inflation channels. Economic Policy Uncertainty and financial volatility added positive effects of 0.24 and 0.18 respectively, indicating that uncertainty shocks and financial stress worsen price instability. Interest rate shocks, by contrast, showed a significant negative effect of −0.38, consistent with conventional monetary transmission theory in which higher policy rates dampen aggregate demand. SHAP-based explainability analysis then revealed how these influences shift across regimes: monetary variables dominate predictions in stable periods, while uncertainty indicators and financial volatility become far more influential during crises.

The framework’s most unconventional element is its blockchain layer, which the authors are careful to distinguish from the predictive engine itself. Blockchain here is not a forecasting tool and was not evaluated with performance metrics. Instead, it functions as a decentralized governance infrastructure. After preprocessing, each dataset version receives a cryptographic fingerprint using the SHA-256 hashing algorithm, and only that hash plus descriptive metadata is recorded on a distributed ledger. Smart contracts validate metadata consistency before new records are appended, and the ledger tracks model training events, hyperparameter configurations, software versions, random seeds, prediction outputs, and even the SHAP explanations attached to each forecast. Because any modification to the underlying data produces a different hash, tampering becomes immediately detectable, and every forecast can be traced back to the exact dataset and model version that produced it.

The authors’ comparative assessment suggests the governance gains are substantial: auditability, traceability, reproducibility, and tamper resistance all improve markedly relative to conventional centralized pipelines, where data revisions and model updates may go partially undocumented. The architecture could also strengthen coordination among central banks, finance ministries, statistical agencies, and international organizations by giving them a shared, transparent validation protocol. The authors acknowledge real trade-offs, however. Cryptographic hashing, smart-contract execution, and network synchronization impose computational overhead, scalability challenges emerge with many participating nodes, and consortium governance demands careful rules for access control and consensus. They also emphasize that the governance benefits described are architectural capabilities rather than experimentally validated performance improvements, since blockchain-specific metrics such as latency and throughput were not empirically tested.

The study’s implications reach well beyond academic benchmarking. As financial systems digitalize and central banks explore Central Bank Digital Currencies, the demand for forecasting systems that are simultaneously accurate, interpretable, and institutionally trustworthy will only grow. By demonstrating that causal machine learning can outperform both classical econometrics and black-box deep learning under structural instability—and that blockchain can wrap such systems in an auditable, tamper-resistant governance shell—the research sketches a plausible blueprint for the next generation of policy-grade economic intelligence. Future work, the authors suggest, will implement the governance layer on a permissioned blockchain platform and explore integrations with decentralized oracles, federated learning, and transformer-based models. If the vision holds, the economies of the future may be forecast not by opaque algorithms or brittle equations, but by transparent, decentralized systems whose every prediction can be verified, explained, and trusted.

Subject of Research: A blockchain-enabled causal machine learning framework for explainable macroeconomic forecasting under structural instability

Article Title: Decentralized blockchain governance for explainable causal AI in macroeconomic forecasting

Article References: Abouzaid, O., & Boussedra, F. (2026). Decentralized blockchain governance for explainable causal AI in macroeconomic forecasting. Frontiers in Blockchain, 9, Article 1883639. https://doi.org/10.3389/fbloc.2026.1883639

Image Credits: AI Generated

DOI: 10.3389/fbloc.2026.1883639

Keywords: blockchain, causal machine learning, Double Machine Learning, macroeconomic forecasting, explainable AI, SHAP, structural breaks, inflation, economic uncertainty, smart contracts, data governance, monetary policy