EconCausal: An R Package That Takes Causal Inference in Time Series Seriously

You can also find this library at CRAN and download it directly from R and RStudio.

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You found a relationship in your data. It’s statistically significant. The coefficients are clean. But here’s the question that keeps rigorous researchers up at night: will this relationship still hold six months from now?

That question — whether a relationship between economic variables is real and durable, not just a fluke of the particular sample you happened to look at — is exactly what the EconCausal R package was built to answer.

What Problem Does EconCausal Solve?

Causal inference in time series data is notoriously tricky. Standard regression can tell you that two variables move together. It cannot tell you why, or whether that co-movement will persist when the economic regime shifts — which it inevitably will.

EconCausal addresses this challenge by combining three well-established econometric methodologies into a single, reproducible pipeline. What makes it distinctive is not any single technique — each component has solid academic pedigree — but rather the orchestration: a standardized protocol that forces every candidate relationship through rigorous pre-testing, proper temporal validation, and explicit decision rules before declaring a finding.

Think of it as a quality-control assembly line for causal claims in time series.

The Three Approaches

EconCausal implements three methodological frameworks, each capturing a different aspect of what “causality” can mean in temporal data. Using all three gives you complementary perspectives rather than relying on a single method’s assumptions.

1. Error Correction Models with MARS (ECM-MARS)

The idea: Many economic variables share a long-run equilibrium relationship but deviate from it in the short run. An Error Correction Model (ECM) captures both dimensions — the long-run cointegrating relationship and the short-run adjustment dynamics.

EconCausal enhances the classical ECM by replacing its usual linear regression engine with Multivariate Adaptive Regression Splines (MARS), a flexible non-parametric method developed by Jerome Friedman in the early 1990s. This matters because real-world adjustment mechanisms are often nonlinear: variables may correct slowly when deviations are small but snap back aggressively when deviations cross certain thresholds. MARS can detect these threshold effects automatically without you having to specify them in advance.

What the protocol does in practice:

• Tests whether the variables are integrated of order one (I(1)) — a prerequisite for cointegration analysis
• Runs both the Engle-Granger and Johansen cointegration tests, applying an “either” rule: if either test finds evidence of cointegration, the analysis proceeds
• Controls for serial correlation and heteroskedasticity using HAC-consistent standard errors
• Fits the ECM with MARS as the regression engine
• Validates the relationship using rolling-origin cross-validation
• Evaluates the model using a dual criterion (more on this below)

Why two cointegration tests? Because Engle-Granger and Johansen have complementary strengths. Engle-Granger is intuitive and straightforward but can miss cointegrating relationships in multivariate settings. Johansen’s approach is more powerful in higher dimensions but relies on stricter distributional assumptions. Requiring only one of them to flag significance is a pragmatic compromise that reduces false negatives without being reckless about false positives.

2. Bayesian Structural Time Series (BSTS)

The idea: Decompose a time series into interpretable structural components — trend, seasonality, regression effects — and do so within a fully Bayesian framework that quantifies uncertainty at every level.

BSTS, originally popularized by Google’s CausalImpact package and the work of Scott and Varian (2014), models the data-generating process as a state-space system. The key innovation in EconCausal’s implementation is the use of spike-and-slab priors for automatic variable selection. This is a Bayesian regularization technique that effectively asks: “Which candidate predictors actually belong in this model?” — and it does so probabilistically, rather than relying on stepwise procedures or arbitrary p-value cutoffs.

What makes it useful for causal inference:

• The structural components absorb confounding patterns (trends, seasonality) that might otherwise masquerade as causal relationships
• Spike-and-slab priors guard against overfitting by aggressively shrinking irrelevant predictors toward zero
• The Bayesian framework produces probabilistic forecasts — not just point predictions, but full posterior distributions — which naturally give you calibrated prediction intervals
• It handles the “what would have happened without the intervention” question, which is central to impact evaluation

3. Bayesian GLM with AR(1) Errors (BGLM-AR1)

The idea: Fit a Bayesian generalized linear model but explicitly account for temporal dependence in the residuals through a first-order autoregressive error structure.

This might sound like a niche technical detail, but it addresses one of the most common mistakes in applied econometrics: ignoring autocorrelation in regression residuals. When residuals are correlated over time, standard errors are wrong, confidence intervals are too narrow, and you end up being more confident in your results than you should be.

By modeling the residual autocorrelation directly with an AR(1) structure and using Hamiltonian Monte Carlo (HMC) sampling via Stan, the BGLM-AR1 approach produces valid inference even when the data have strong temporal dependence. The priors are weakly informative and calibrated for standardized variables, which means they provide regularization without dominating the data.

The dual evaluation criterion: This is where EconCausal’s protocol really distinguishes itself. A relationship is only accepted if it improves both:

1. Predictive density (measured by Expected Log Predictive Density, or ELPD, estimated via Pareto-Smoothed Importance Sampling Leave-One-Out cross-validation — PSIS-LOO)
2. Point forecast accuracy (measured by RMSE)

Requiring improvement on both dimensions simultaneously is stricter than most practitioners apply. A model might improve point forecasts while worsening the calibration of its uncertainty estimates, or vice versa. EconCausal demands both.

What Makes EconCausal Different: The Protocol

You might be thinking: “Most of these individual techniques already exist in other R packages — brms and Stan for Bayesian modeling, the ecm package for error correction models, bsts and CausalImpact for structural time series.” And you’d be right.

The novelty of EconCausal is not in inventing new statistical methods. It lies in integrating them into a single, standardized protocol with explicit decision rules, thresholds, and validation procedures that run automatically.

Here’s what that means concretely:

Temporal Stability Validation

Rather than evaluating a model on a single train-test split, EconCausal uses Leave-Future-Out (LFO) cross-validation with configurable windows and horizons. The system repeatedly:

1. Trains on data up to a cutoff point
2. Forecasts into the future
3. Evaluates forecast accuracy
4. Slides the cutoff forward
5. Repeats

A relationship is only declared “stable” if it performs consistently across multiple temporal folds — not just one lucky window. The package reports a support metric: the proportion of temporal folds where the relationship holds up.

Dual Decision Rules with Explicit Thresholds

Every candidate relationship must clear both the Bayesian bar (ELPD improvement) and the frequentist bar (RMSE reduction). These are not vague guidelines — the protocol applies explicit numerical thresholds and includes tie-breaking rules for ambiguous cases.

A Single Reproducible Pipeline

From data loading through pre-testing, model fitting, temporal validation, and final go/no-go decisions — everything runs in one pipeline. This eliminates the researcher degrees of freedom that plague so many applied studies, where hundreds of analytical choices are made invisibly along the way.

An independent novelty assessment included in the package’s documentation compared this protocol against both the academic literature and existing software frameworks. The finding: while all the building blocks are state-of-the-art and well-documented in the literature, no existing academic paper or software package was found that implements the same end-to-end protocol with the same combination of pre-tests, dual decision rules, support thresholds, and temporal validation.

Who Should Use EconCausal?

The package was originally designed for economic research — specifically, examining production-circulation relationships — but its applicability extends to any domain where you need to answer questions like:

• Does X cause Y, or is it the other way around? The temporal structure of the methods naturally distinguishes between “X leads Y” and “Y leads X.”
• Is this relationship robust across time? The rolling-origin validation directly tests temporal stability.
• Are there nonlinear dynamics at play? The MARS component in ECM-MARS can detect threshold effects and nonlinear adjustment speeds.
• How certain can we be? The Bayesian components (BSTS and BGLM-AR1) provide full uncertainty quantification, not just point estimates.

This makes it useful for:

• Academic researchers conducting empirical macroeconomics, financial economics, or applied econometrics
• Policy analysts evaluating whether proposed policy relationships will hold across different economic conditions
• Data scientists in finance who need to move beyond correlation to directionality and stability
• Graduate students learning causal inference methods who want a pedagogical tool that enforces methodological discipline

Getting Started

Installation is straightforward:

# Install from GitHub
remotes::install_github("IsadoreNabi/EconCausal")
# On Windows, if vignette building fails:
remotes::install_github("IsadoreNabi/EconCausal", build_vignettes = FALSE)

The package depends on a modern Bayesian computing stack — cmdstanr for Hamiltonian Monte Carlo sampling, loo for PSIS-LOO computation, and bsts for structural time series components — so make sure your R environment is up to date.

A Note on What EconCausal Is Not

It’s worth being explicit about the boundaries:

• It is not a magic wand. If your data are fundamentally unsuitable for causal inference (too few observations, structural breaks that dominate the signal, measurement error in the variables), EconCausal will not rescue you. Its value lies in applying rigorous methods correctly and transparently — not in overcoming the limitations of your data.
• It does not replace domain knowledge. Statistical evidence for a stable, directional relationship is necessary but not sufficient for a causal claim. You still need a theoretical mechanism, an understanding of potential confounders, and judgment about the plausibility of the identifying assumptions.
• It does not invent new mathematics. Its contribution is the protocol — the disciplined, reproducible integration of established techniques into a pipeline with explicit, auditable decision rules.

The Bottom Line

EconCausal occupies a valuable niche in the R ecosystem. While individual components of its methodology are available elsewhere — Bayesian GLMs in brms, BSTS in the bsts package, ECM in the ecm package — no other tool packages them into a single protocol that systematically tests temporal stability, applies dual evaluation criteria, and produces reproducible, auditable results.

If your work involves making causal claims from time series data — and you care about whether those claims will survive contact with the future — EconCausal deserves a serious look.

Repository: github.com/IsadoreNabi/EconCausal

References for the underlying methodologies: Engle & Granger (1987), Johansen (1991), Friedman (1991), Scott & Varian (2014), Vehtari et al. (2017), Hyndman & Athanasopoulos (2021). See the package documentation for the complete bibliography.