REVIEW 7 cited by
Applied Causal Inference Powered by ML and AI
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.
Forward citations
Cited by 7 Pith papers
-
A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity
A locally robust, cross-fitted omnibus test detects systematic treatment-effect heterogeneity with respect to low-dimensional covariates while allowing high-dimensional ML-based nuisance estimation across unconfounded...
-
Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases
A SQL-like domain-specific language that declares predictive tasks on relational databases and automatically generates leakage-free training labels, with batch and low-latency implementations.
-
Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies
A two-period double machine learning evaluation of Swiss labor market programs finds temporary wage subsidies the most effective programs when modeled as dynamic policies.
-
Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
Three Bayesian TMLE formulations (mean-based, summary-statistic, and joint Bayesian-network) produce posterior distributions of the average treatment effect; the joint version shows higher coverage than classical TMLE...
-
From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
A causal framework for learning treatment policies with deferral, applied to diuretic dosing in acute heart failure with kidney injury.
-
Strategic A/B testing via Maximum Probability-driven Two-armed Bandit
A weighted two-armed bandit test statistic is claimed to concentrate more under the null and less under the alternative than the classical z-test, improving power for detecting small treatment effects in A/B tests.
-
Shrinkage-Based Regressions with Many Related Treatments
A customized ridge regression with an unpenalized focal treatment effect produces lower-variance estimates for many sparse sub-treatments and exactly recovers the single-treatment estimator.
Discussion (0). Sign in to comment.