Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition (1707.02641v5)

Abstract: Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.

• The paper reveals that flexible, non-parametric models, such as BART and ensemble methods, tend to outperform other techniques in estimating causal effects.
• Evaluations focus on key metrics like bias, RMSE, and coverage, emphasizing the trade-off between model flexibility and fidelity.
• The study underlines challenges in method selection amid data heterogeneity and complex treatment-response relationships, urging further methodological innovation.

Evaluation of Causal Inference Methods in a Competition Setting

The paper "Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition" presents a meticulous examination of causal inference methodologies as assessed during the 2016 Atlantic Causal Inference Conference (ACIC) competition. The authors organized the competition to evaluate both automated and manually tuned approaches to estimating causal effects from observational data, focusing on the Sample Average Treatment effect on the Treated (SATT).

Design and Motivation

The paper aims to address the challenge of selecting appropriate causal inference methods among numerous available strategies. Common issues include limited method comparisons and potential biases in published performance results due to selective reporting. The competition structure aimed to provide a more holistic view by comparing 30 methods across diverse simulated datasets. The use of real-world covariates to simulate data ensured relevance to practical settings, while knobs varied degrees of nonlinearity, overlap, treatment heterogeneity, and alignment between the treatment assignment mechanism and the response surface.

Simulation and Evaluation Framework

The paper's paramount contribution is the creation of nuanced testing grounds that emulate a variety of real-world complexities in causal inference tasks. Evaluations focused on balancing parametric assumptions’ flexibility with modeling fidelity, an essential trade-off in causal analysis. Methods were judged not only on bias and RMSE but also coverage, interval length, and the precision of estimating heterogeneous effects (PEHE).

Results and Insights

The analysis conclusively indicates that methods capable of flexibly modeling the response surface, such as Bayesian Additive Regression Trees (BART) and ensemble methods like Super Learner with TMLE (Targeted Maximum Likelihood Estimation), generally outperform competitors across scenarios. The challenge of matching performance to specific settings, given limited overlap or misalignment of the treatment and response model, revealed critical areas for future methodological emphasis. Specifically, the use of non-parametric models to adapt to nonlinearities and variable treatment effects emerged as a pivotal factor in robust causal inference.

Examination of Method Features

Key features distinguishing successful methods include non-parametric modeling of the response surface, propensity score estimation, and ensemble techniques that aggregate predictions across multiple algorithms. While these features contribute significantly to performance, the paper underscores the difficulty in predicting exact method suitability across varying settings, highlighting the nuanced interdependence between data characteristics and method efficacy.

Implications and Future Directions

The findings advocate for continued exploration and refinement of adaptive, non-parametric causal inference methods. Given the unsolved challenges—such as achieving reliable coverage and computational efficiency in high-dimensional settings—there is ample room for innovation. Future efforts might expand on the competition model to involve varied datasets with differing covariate distributions, non-binary treatments, and real-world non-IID data structures.

Through the synthesis of competition-based evaluations, this paper provides a comprehensive assessment that advances understanding in automated versus manual causal inference methods. It lays a foundation for pragmatic methodological guidance while establishing an empirical framework that researchers can adapt and build upon in subsequent exploration of causal inference challenges.