The Blessings of Multiple Causes (1805.06826v3)

Abstract: Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that combines unsupervised machine learning and predictive model checking to perform causal inference in multiple-cause settings. The deconfounder infers a latent variable as a substitute for unobserved confounders and then uses that substitute to perform causal inference. We develop theory for the deconfounder, and show that it requires weaker assumptions than classical causal inference. We analyze its performance in three types of studies: semi-simulated data around smoking and lung cancer, semi-simulated data around genome-wide association studies, and a real dataset about actors and movie revenue. The deconfounder provides a checkable approach to estimating closer-to-truth causal effects.

• The paper introduces the deconfounder, which leverages latent variable modeling to mitigate confounding in multiple-cause studies.
• It combines unsupervised machine learning with predictive model checking to deliver robust causal estimates under weaker assumptions.
• Empirical validation on semi-synthetic and real-world data demonstrates enhanced accuracy over traditional causal inference methods.

Overview of "The Blessings of Multiple Causes"

This paper presents a distinctive approach to causal inference in observational studies, which involves the estimation of causal effects when dealing with multiple causes. Traditional causal inference often relies on the assumption of ignorability, where all confounders between the causes and outcomes are observed. However, this assumption is notoriously stringent and untestable. The authors propose a novel algorithm called the "deconfounder," which provides a method for robust causal inference under weaker assumptions by leveraging the presence of multiple causes.

Key Contributions

The deconfounder combines unsupervised machine learning techniques with predictive model checking to estimate causal effects. It operates under the premise that in multiple-cause scenarios, identifying a latent variable can serve as a substitute for unobserved confounders. The key steps include:

1. Modeling with Latent Variables: The deconfounder relies on finding a factor model that best describes the assignment mechanism of the causes, capturing the intricate dependency structure between them.
2. Substitute Confounding: By inferring latent variables (substitute confounders), the model conditions causal estimates on these, effectively controlling for unobserved confounders that influence multiple causes simultaneously.
3. Performance Evaluation: Through theoretical expositions, the deconfounder is shown to require weaker assumptions than classical approaches, specifically obviating the need for strict ignorability by only assuming "single-cause" ignorability.

Empirical Validation

The authors validate the deconfounder using both semi-synthetic data and real-world datasets. In their studies:

• Semi-Synthetic Data: They illustrate the method on datasets concerning smoking and expenses, and genetics (GWAS). It is shown that the deconfounder provides more accurate causal estimates than traditional methods that do not account for latent confounders.
• Real Data: In a case paper involving actors and movie revenues, the deconfounder is used to estimate how individual actors influence box office earnings. It reveals shifts in significant predictors once latent confounding structures are accounted for.

Theoretical Implications and Future Directions

The deconfounder significantly impacts both the theoretical and applied aspects of causal inference. It underscores the potential of using latent variables and flexible modeling assumptions to mitigate the confounding bias in complex real-world settings. Moreover, it opens up new avenues for addressing causal questions within domains that naturally involve multiple factors, such as medical informatics, social sciences, and neurosciences.

Looking ahead, the research invites further refinement in model selection and predictive checking procedures to enhance the robustness of causal estimates. The development of richer theory around substitute confounders, their computational efficiency, and applicability across diverse datasets represents a vibrant frontier.

Conclusion

This work addresses long-standing challenges in observational causal inference by harnessing the multiplicity of causes as a methodological asset rather than an impediment. The deconfounder stands out as a promising tool for researchers tasked with disentangling complex cause-effect relationships without complete observational data on potential confounders. Its reliance on testable modeling assumptions and applicability to a range of scientific inquiries underscore its utility and relevance in advancing the field of causal inference.