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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: Ko'p o'zgaruvchan kriptovalyuta seriyasida sabablarni aniqlash
    Aug 18, 2026 #causal-inference

    PCMCI: Ko'p o'zgaruvchan kriptovalyuta seriyasida sabablarni aniqlash

    PCMCI ning ikki bosqichli MCI testi qanday qilib korrelyatsiya va ikki o'zgaruvchan Granger qila olmaydigan kripto aktivlari o'rtasidagi yo'naltirilgan sababiy bog'lanishni tiklaydi - qurilish, tigramit quvur liniyasi va unga hali kerak bo'lgan real ma'lumotlarni o'rganish.
  4. 04
    Savdo-sotiqda Heterogen Davolash Ta'sirlari uchun Kausal O'rmonlar
    Aug 1, 2026 #causal-inference

    Savdo-sotiqda Heterogen Davolash Ta'sirlari uchun Kausal O'rmonlar

    Ushbu blogdagi har bir backtest shartli o'rtacha qiymatni baholaydi. Kausal o'rmonlar buning o'rniga shartli davolash effektini baholaydi — tau(x) o'rniga mu(x) — vijdlan bo'linish, moslashuvchan yadro og'irliklari va topgan heterogennilik haqiqiymi yoki yo'qmi aytadigan kalibrlash testi bilan.
  5. 05
    Double Machine Learning: Returninglarni Bashorat Qilish O'rniga Sababiy Parametrni Baholash
    Aug 2, 2026 #causal-inference

    Double Machine Learning: Returninglarni Bashorat Qilish O'rniga Sababiy Parametrni Baholash

    Bu blogdagi barcha modellar hozirgacha 'nimani nimani bashorat qiladi?' savolini javob beradi. Double ML 'nimaga nima sabab bo'ladi?' savolini javob beradi — himoya qila oladigan standart xato bilan. Qisman chiziqli model, Neyman ortogonaliteti, order book ma'lumotlarida tozalandirilgan cross-fitting va haqiqiy DML ishonch oralig'i faqat bitta oldindan belgilangan savolga nisbatan omon qolishiga oid sharhli hisobot.
  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.