On the Estimation Accuracy of Causal Effects using Supplementary Variables

2015 ◽  
Vol 43 (2) ◽  
pp. 505-519 ◽  
Author(s):  
Manabu Kuroki ◽  
Takahiro Hayashi
Keyword(s):  
Causal Effects ◽  
Estimation Accuracy ◽  
Supplementary Variables

scholarly journals A Bayesian Approach for Estimating Causal Effects from Observational Data

2020 ◽  
Vol 34 (04) ◽  
pp. 5395-5402
Author(s):  
Johan Pensar ◽  
Topi Talvitie ◽  
Antti Hyttinen ◽  
Mikko Koivisto
Keyword(s):  
Bayesian Method ◽  
Causal Relation ◽  
Causal Effect ◽  
Small Sample ◽  
Causal Effects ◽  
Estimation Accuracy ◽  
Flow Cytometry Data ◽  
Mere Existence ◽  
Effect Estimation ◽  
Small Sample Sizes

We present a novel Bayesian method for the challenging task of estimating causal effects from passively observed data when the underlying causal DAG structure is unknown. To rigorously capture the inherent uncertainty associated with the estimate, our method builds a Bayesian posterior distribution of the linear causal effect, by integrating Bayesian linear regression and averaging over DAGs. For computing the exact posterior for all cause-effect variable pairs, we give an algorithm that runs in time O(3d d) for d variables, being feasible up to 20 variables. We also give a variant that computes the posterior probabilities of all pairwise ancestor relations within the same time complexity, significantly improving the fastest previous algorithm. In simulations, our Bayesian method outperforms previous methods in estimation accuracy, especially for small sample sizes. We further show that our method for effect estimation is well-adapted for detecting strong causal effects markedly deviating from zero, while our variant for computing posteriors of ancestor relations is the method of choice for detecting the mere existence of a causal relation. Finally, we apply our method on observational flow cytometry data, detecting several causal relations that concur with previous findings from experimental data.

Exploring a causal model in observational cohort data: The role of parents and peers in shaping substance use trajectories

2020 ◽  
Author(s):  
Christopher Greenwood ◽  
George Joseph Youssef ◽  
Primrose Letcher ◽  
Elizabeth Spry ◽  
Lauryn Hagg ◽  
...  
Keyword(s):  
Substance Use ◽  
Cannabis Use ◽  
Causal Effects ◽  
Deviant Peers ◽  
Adolescent Relationships ◽  
Cohort Data ◽  
Observational Cohort ◽  
Peer Affiliations ◽  
Role Of Parents ◽  
Adolescent Relationship

Aims: To explore the process of applying counterfactual thinking in examining causal predictors of substance use trajectories in observational cohort data. Specifically, we examine the extent to which quality of the parent-adolescent relationship and affiliations with deviant peers are causally related to trajectories of alcohol, tobacco, and cannabis use across adolescence and into young adulthood. Methods: Data were drawn from the Australian Temperament Project, a population-based cohort study that has followed a sample of young Australians from infancy to adulthood since 1983. Parent-adolescent relationship quality and deviant peer affiliations were assessed at age 13-14 years. Latent curve models were fitted for past month alcohol, tobacco, and cannabis use (n = 1,590) from age 15-16 to 27-28 years (5 waves). Confounding factors were selected in line with the counterfactual framework. Results: Following confounder adjustment, higher quality parent-adolescent relationships were associated with lower baseline cannabis use, but not alcohol or tobacco use trajectories. In contrast, affiliations with deviant peers were associated with higher baseline binge drinking, tobacco, and cannabis use, and an earlier peak in the cannabis use trajectory. Conclusions: Confounding adjustments weakened several estimated associations and the interpretation of such associations as causal is not without limitations. Nevertheless, findings suggested causal effects of both parent-adolescent relationships and deviant peer affiliations on the trajectory of substance use. Causal effects were however more pervasive (i.e., more substance types) and protracted for deviant peer affiliations. The current study encourages the exploration of causal relationships in observational cohort data, when relevant limitations are transparently acknowledged.

Bounding Causal Effects in Ecological Inference Problems

2015 ◽  
Author(s):  
Alejandro Corvalan ◽  
Emerson Melo ◽  
Robert P Sherman ◽  
Matthew Shum
Keyword(s):  
Causal Effects ◽  
Ecological Inference ◽  
Inference Problems
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