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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 Granger yang Berbeda: Apakah Lead-Lag BTC Bertahan?
    Aug 7, 2026 #causal-inference

    Toda-Yamamoto vs Granger yang Berbeda: Apakah Lead-Lag BTC Bertahan?

    Kausalitas Granger pada harga kripto dilakukan dengan dua cara — perbedaan pengembalian dan level Toda-Yamamoto — dengan implementasi Wald yang benar, matriks kausalitas terkoreksi-N yang efektif, dan uji stabilitas bergulir untuk mengetahui apakah lag dapat diperdagangkan atau tidak.
  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
    Causal Forests untuk Efek Perawatan Heterogen dalam Trading
    Aug 1, 2026 #causal-inference

    Causal Forests untuk Efek Perawatan Heterogen dalam Trading

    Setiap backtest di blog ini memperkirakan rata-rata bersyarat. Causal forests memperkirakan efek perawatan bersyarat sebagai gantinya — tau(x) bukan mu(x) — dengan honest splitting, representasi bobot kernel adaptif, dan tes kalibrasi yang memberi tahu Anda apakah heterogenitas yang Anda temukan nyata.
  5. 05
    Double Machine Learning: Mengestimasi Parameter Kausal Alih-alih Memprediksi Return
    Aug 2, 2026 #causal-inference

    Double Machine Learning: Mengestimasi Parameter Kausal Alih-alih Memprediksi Return

    Setiap model di blog ini menjawab 'apa yang memprediksi apa?'. Double ML menjawab 'apa yang menyebabkan apa?' — dengan standar error yang bisa Anda pertahankan. Model partially linear, orthogonalitas Neyman, cross-fitting yang dipurges pada data order book, dan perhitungan jujur mengapa interval kepercayaan DML yang valid bertahan untuk tepat satu pertanyaan yang telah ditentukan sebelumnya.
  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.