Causal Machine Learning

Peter Spirtes et al. (1993) — Springer · 2780 citations

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.

Xun Zheng et al. (2018) · 1250 citations

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.

Marcos Lopez de Prado (2022) · 13 citations

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.

Emanuele Olivetti et al. (2026)

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.