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🔬 7 parts

Causality & Lead-Lag in Markets

Who moves whom — Granger causality, transfer entropy, PCMCI discovery, and the causal ML toolbox (double ML, causal forests, synthetic control) applied to crypto markets.

  1. 01
    Toda-Yamamoto vs Differenced Granger: Does the BTC Lead-Lag Survive?
    Aug 7, 2026 #causal-inference

    Toda-Yamamoto vs Differenced Granger: Does the BTC Lead-Lag Survive?

    Granger causality on crypto prices done two ways — differenced returns and Toda-Yamamoto on levels — with a correct Wald implementation, an effective-N corrected causality matrix, and a rolling-stability test of whether the lag is tradeable at all.
  2. 02
    Transfer Entropy: Which Way Does Information Flow Between Crypto Assets?
    Aug 24, 2026 #causal-inference

    Transfer Entropy: Which Way Does Information Flow Between Crypto Assets?

    DCC-GARCH tells you when crypto dependence tightens. Transfer entropy tells you which way it points. A directed information-flow measure, its null calibration, and an honest account of what it does and does not add over average pairwise correlation.
  3. 03
    PCMCI: Causal Discovery in Multivariate Crypto Time Series
    Aug 18, 2026 #causal-inference

    PCMCI: Causal Discovery in Multivariate Crypto Time Series

    How PCMCI's two-stage MCI test recovers directed causal links between crypto assets where correlation and bivariate Granger cannot — the construction, the tigramite pipeline, and the real-data study it still needs.
  4. 04
    Bosques causales para efectos de tratamiento heterogéneos en trading
    Aug 1, 2026 #causal-inference

    Bosques causales para efectos de tratamiento heterogéneos en trading

    Cada backtest en este blog estima una media condicional. Los bosques causales estiman un efecto de tratamiento condicional en su lugar — tau(x) en lugar de mu(x) — con división honesta, una representación de pesos de núcleo adaptativo y una prueba de calibración que te dice si la heterogeneidad que encontraste es real.
  5. 05
    Double Machine Learning: Estimando un Parametro Causal en Vez de Predecir Retornos
    Aug 2, 2026 #causal-inference

    Double Machine Learning: Estimando un Parametro Causal en Vez de Predecir Retornos

    Todos los modelos en este blog hasta ahora responden 'que predice que'. Double ML responde 'que causa que' — con un error estandar que puedes defender. El modelo parcialmente lineal, ortogonalidad de Neyman, cross-fitting purgado en datos de libro de ordenes y un relato honesto de por que un intervalo de confianza DML valido sobrevive exactamente a una pregunta preespecificada.
  6. 06
    The Other Way a Regression Lies: Endogeneity, 2SLS, and the Gamma Calibration Problem
    Aug 10, 2026 #causal-inference

    The Other Way a Regression Lies: Endogeneity, 2SLS, and the Gamma Calibration Problem

    Selection bias in the search is not the only way a regression fools you. When the regressor is correlated with the error, more data makes the estimate more confidently wrong. Instrumental variables applied to the one endogeneity problem this blog has already left open: permanent impact from net taker flow.
  7. 07
    Synthetic Control Methods for Evaluating Trading Strategies
    Aug 21, 2026 #causal-inference

    Synthetic Control Methods for Evaluating Trading Strategies

    This series has priced the selection route to a false edge — DSR prices the winner, PBO prices the search. Neither touches confounding: the strategy that made money because volatility doubled the week you deployed it. The Synthetic Control Method builds a weighted counterfactual from a donor pool of untouched instruments and gives you a falsification criterion and a placebo p-value.