This notebook demonstrates why outcome choice matters for causal credibility. We analyze the same treatment (extreme crypto funding) with two different outcomes, showing that the robustness of causal claims depends critically on the tightness of the mechanism connecting treatment to outcome.
03 Econml Dml
This notebook implements Double Machine Learning (DML) to estimate the causal effect of momentum signals on forward returns, controlling for complex confounders. We demonstrate how to move beyond correlation to establish whether momentum has genuine predictive power.
04 Dml Crypto Regime
Book Reference: Chapter 15, §15.2 (DML with Regime Heterogeneity) Uses modeling_dataset data.
05 Momentum Causal Trading
This notebook demonstrates how causal inference methodology applies to trading research, with honest assessment of what it can and cannot prove. Uses modeling_dataset data.
11 Factor Zoo Validation
This notebook applies the double-selection LASSO framework from Feng, Giglio, and Xiu (2020) to test whether candidate factors have genuine marginal pricing power after controlling for existing factors. This connects the causal inference machinery of this chapter to the latent factor models of Chapter 14.
06 Fed Announcement Bsts
This notebook implements a Bayesian Structural Time-Series (BSTS) event study to measure the causal impact of Federal Reserve announcements on bond ETFs. We demonstrate counterfactual construction, control selection, and validation through placebo tests - the complete event study workflow.
07 Tigramite Time Series
This notebook demonstrates causal discovery - learning the causal structure from time series data without specifying a DAG a priori. We compare PCMCI with traditional Granger causality and show how to discover lead-lag relationships in multi-asset financial time series.
This notebook explores the ADIA Lab Causal Discovery Challenge (Olivetti et al., 2026) as a cautionary, clarifying example of what synthetic benchmarks can and cannot demonstrate about causal discovery. Uses synthetic data.
10 Case Study Insights
This notebook loads DML causal estimates from all nine case studies, verifies completeness, and extracts insights about when causal identification succeeds or fails across asset classes. Uses causal_from_registry data.
1 primer providing foundational concepts for this chapter.
Guido W. Imbens and Joshua D. Angrist (1994) — Econometrica · 5082 citations
This paper introduces the concept of Local Average Treatment Effect (LATE) as an alternative to Average Treatment Effect (ATE) when evaluating social programs, especially in cases where full random assignment is not feasible, providing a method to identify treatment effects for subpopulations influenced by an instrumental variable.
Aapo Hyvärinen et al. (2010) — Journal of Machine Learning Research · 440 citations
This paper introduces a novel method for estimating Structural Vector Autoregression (SVAR) models by leveraging the non-Gaussianity of disturbance variables, overcoming identifiability issues common in traditional SVAR estimation.
David H. Bailey and Marcos Lopez de Prado (2014) · 110 citations
The paper introduces the Deflated Sharpe Ratio (DSR), a statistic that adjusts performance metrics for the probability of backtest overfitting caused by multiple testing and non-normal returns.
Kay H. Brodersen et al. (2015) — The Annals of Applied Statistics · 854 citations
The paper introduces a fully Bayesian state-space (structural time-series) approach to estimate the causal effect of an intervention by forecasting a counterfactual time series (synthetic control) and comparing it to observed outcomes, implemented in the CausalImpact R package.
This paper introduces a novel method called NOTEARS for learning directed acyclic graphs (DAGs) by reformulating the combinatorial optimization problem into a continuous one, enabling the use of standard numerical algorithms.
Victor Chernozhukov et al. (2018) — The Econometrics Journal · 3298 citations
This paper shows how to get valid (root-N, asymptotically normal) inference for causal/structural parameters when nuisance components are learned with high-dimensional ML, by combining Neyman-orthogonal scores with cross-fitting.
Jakob Runge et al. (2019) — Nature Communications · 904 citations
This Perspective surveys modern methods for causal discovery from time series (beyond correlation/forecasting) and maps them to recurring real-world problems—arguing for benchmarks and clearer assumptions to make causal claims credible in complex dynamical systems.
Guanhao Feng et al. (2020) — The Journal of Finance · 490 citations
The authors propose a Double-Selection LASSO methodology to evaluate new asset pricing factors against hundreds of existing ones, finding that while most new factors are redundant, profitability and investment factors provide significant marginal value.
Alexander Reisach et al. (2021) — Curran Associates, Inc. · 182 citations
This paper demonstrates that the performance of continuous causal structure learning algorithms on simulated data can be attributed to a property called 'varsortability,' where marginal variance increases along the causal order, and that this advantage disappears when data is standardized.
The paper argues that current factor investing relies on spurious associational correlations rather than causal mechanisms, and proposes adopting 'Causal Factor Investing' using causal graphs and do-calculus to distinguish true drivers of returns from statistical artifacts.
Ali Shojaie and Emily B. Fox (2022) — Annual Review of Statistics and Its Application · 458 citations
This review explains what Granger causality really measures (predictive, time-ordered dependence), why naive/bivariate VAR tests can mislead, and how modern high-dimensional, nonlinear, discrete-valued, and mixed-frequency methods extend the framework.
This paper analyzes the results of the ADIA Lab Causal Discovery Challenge, finding that supervised learning methods, sophisticated feature engineering, and ensemble methods significantly outperformed traditional constraint-based approaches in inferring causal relationships from synthetic data.