Causal Effect Random Forest of Interaction Trees for Learning Individualized Treatment Regimes with Multiple Treatments in Observational Studies

Stat ◽  
2022 ◽  
Author(s):  
Luo Li ◽  
Richard A. Levine ◽  
Juanjuan Fan
Keyword(s):  
Random Forest ◽  
Observational Studies ◽  
Causal Effect ◽  
Individualized Treatment ◽  
Treatment Regimes ◽  
Multiple Treatments

scholarly journals Identifiability of causal effects with multiple causes and a binary outcome

Biometrika ◽  
2021 ◽  
Author(s):  
Dehan Kong ◽  
Shu Yang ◽  
Linbo Wang
Keyword(s):  
Observational Studies ◽  
Choice Model ◽  
Causal Effect ◽  
Gaussian Model ◽  
Estimation Procedure ◽  
Outcome Variable ◽  
Identification Approach ◽  
Multiple Treatments ◽  
Binary Choice Model ◽  
Probit Link

Abstract Unobserved confounding presents a major threat to causal inference from observational studies. Recently, several authors suggest that this problem may be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that the causal effect is indeed identifiable if we assume a general binary choice model for the outcome with a non-probit link. Our identification approach is based on the incongruence between Gaussianity of the treatments and latent confounder, andnon-Gaussianity of a latent outcome variable. We further develop a two-step likelihood-based estimation procedure.

scholarly journals Systematic survey of the causal language use in systematic reviews of observational studies: a study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e038571
Author(s):  
Mi Ah Han ◽  
Gordon Guyatt
Keyword(s):  
Systematic Reviews ◽  
Observational Studies ◽  
Language Use ◽  
Causal Effect ◽  
Data Extraction ◽  
Open Science ◽  
Third Party ◽  
Systematic Survey ◽  
Assessment Development ◽  
Causal Language

IntroductionSometimes, observational studies may provide important evidence that allow inferences of causality between exposure and outcome (although on most occasions only low certainty evidence). Authors, frequently and perhaps usually at the behest of the journals to which they are submitting, avoid using causal language when addressing evidence from observational studies. This is true even when the issue of interest is the causal effect of an intervention or exposure. Clarity of thinking and appropriateness of inferences may be enhanced through the use of language that reflects the issue under consideration. The objectives of this study are to systematically evaluate the extent and nature of causal language use in systematic reviews of observational studies and to relate that to the actual intent of the investigation.Methods and analysisWe will conduct a systematic survey of systematic reviews of observational studies addressing modifiable exposures and their possible impact on patient-important outcomes. We will randomly select 200 reviews published in 2019, stratified in a 1:1 ratio by use and non-use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Teams of two reviewers will independently assess study eligibility and extract data using a standardised data extraction forms, with resolution of disagreement by discussion and, if necessary, by third party adjudication. Through examining the inferences, they make in their papers’ discussion, we will evaluate whether the authors’ intent was to address causation or association. We will summarise the use of causal language in the study title, abstract, study question and results using descriptive statistics. Finally, we will assess whether the language used is consistent with the intention of the authors. We will determine whether results in reviews that did or did not use GRADE differ.Ethics and disseminationEthics approval for this study is not required. We will disseminate the results through publication in a peer-reviewed journals.RegistrationOpen Science Framework (osf.io/vh8yx).

scholarly journals Novel bounds for causal effects based on sensitivity parameters on the risk difference scale

2021 ◽  
Vol 9 (1) ◽  
pp. 190-210
Author(s):  
Arvid Sjölander ◽  
Ola Hössjer
Keyword(s):  
Risk Ratio ◽  
Simulation Study ◽  
Observational Studies ◽  
Causal Effect ◽  
Real Data ◽  
Risk Difference ◽  
Causal Effects ◽  
Ratio Scale ◽  
Unmeasured Confounding ◽  
Range Of Values

Abstract Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E-value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E-value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study.

scholarly journals Causal Association Between Heart Failure and Alzheimer’s Disease: A Two-Sample Bidirectional Mendelian Randomization Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenglin Duan ◽  
Jingjing Shi ◽  
Guozhen Yuan ◽  
Xintian Shou ◽  
Ting Chen ◽  
...  
Keyword(s):  
Heart Failure ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sensitivity Analysis ◽  
Causal Relationship ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Relationships ◽  
Simple Mode

Background: Traditional observational studies have demonstrated an association between heart failure and Alzheimer’s disease. The strengths of observational studies lie in their speed of implementation, cost, and applicability to rare diseases. However, observational studies have several limitations, such as uncontrollable confounders. Therefore, we employed Mendelian randomization of genetic variants to evaluate the causal relationships existing between AD and HF, which can avoid these limitations.Materials and Methods: A two-sample bidirectional MR analysis was employed. All datasets were results from the UK’s Medical Research Council Integrative Epidemiology Unit genome-wide association study database, and we conducted a series of control steps to select the most suitable single-nucleotide polymorphisms for MR analysis, for which five primary methods are offered. We reversed the functions of exposure and outcomes to explore the causal direction of HF and AD. Sensitivity analysis was used to conduct several tests to avoid heterogeneity and pleiotropic bias in the MR results.Results: Our MR studies did not support a meaningful causal relationship between AD on HF (MR-Egger, p = 0.634 > 0.05; weighted median (WM), p = 0.337 > 0.05; inverse variance weighted (IVW), p = 0.471 > 0.05; simple mode, p = 0.454 > 0.05; weighted mode, p = 0.401 > 0.05). At the same time, we did not find a significant causal relationship between HF and AD with four of the methods (MR-Egger, p = 0.195 > 0.05; IVW, p = 0.0879 > 0.05; simple mode, p = 0.170 > 0.05; weighted mode, p = 0.110 > 0.05), but the WM method indicated a significant effect of HF on AD (p = 0.025 < 0.05). Because the statistical powers of IVW and MR-Egger are more than that of WM, we think that there is no causal effect of HF on AD. Sensitivity analysis and horizontal pleiotropy were not detected in the MR analysis.Conclusion: Our results did not provide significant evidence indicating any causal relationships between HF and AD in the European population. Therefore, more large-scale datasets or datasets related to similar factors are expected for further MR analysis.

scholarly journals Fetal Origins of Mental Disorders? An Answer Based on Mendelian Randomization

2018 ◽  
Vol 21 (6) ◽  
pp. 485-494 ◽  
Author(s):  
Subhi Arafat ◽  
Camelia C. Minică
Keyword(s):  
Mental Disorders ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Current Evidence ◽  
Genome Wide Association Studies ◽  
Fetal Origins ◽  
Barker Hypothesis ◽  
Main Criticism

The Barker hypothesis states that low birth weight (BW) is associated with higher risk of adult onset diseases, including mental disorders like schizophrenia, major depressive disorder (MDD), and attention deficit hyperactivity disorder (ADHD). The main criticism of this hypothesis is that evidence for it comes from observational studies. Specifically, observational evidence does not suffice for inferring causality, because the associations might reflect the effects of confounders. Mendelian randomization (MR) — a novel method that tests causality on the basis of genetic data — creates the unprecedented opportunity to probe the causality in the association between BW and mental disorders in observation studies. We used MR and summary statistics from recent large genome-wide association studies to test whether the association between BW and MDD, schizophrenia and ADHD is causal. We employed the inverse variance weighted (IVW) method in conjunction with several other approaches that are robust to possible assumption violations. MR-Egger was used to rule out horizontal pleiotropy. IVW showed that the association between BW and MDD, schizophrenia and ADHD is not causal (all p > .05). The results of all the other MR methods were similar and highly consistent. MR-Egger provided no evidence for pleiotropic effects biasing the estimates of the effects of BW on MDD (intercept = -0.004, SE = 0.005, p = .372), schizophrenia (intercept = 0.003, SE = 0.01, p = .769), or ADHD (intercept = 0.009, SE = 0.01, p = .357). Based on the current evidence, we refute the Barker hypothesis concerning the fetal origins of adult mental disorders. The discrepancy between our results and the results from observational studies may be explained by the effects of confounders in the observational studies, or by the existence of a small causal effect not detected in our study due to weak instruments. Our power analyses suggested that the upper bound for a potential causal effect of BW on mental disorders would likely not exceed an odds ratio of 1.2.

scholarly journals Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data

2018 ◽  
Vol 115 (11) ◽  
pp. 2571-2577 ◽  
Author(s):  
Martijn J. Schuemie ◽  
George Hripcsak ◽  
Patrick B. Ryan ◽  
David Madigan ◽  
Marc A. Suchard
Keyword(s):  
Gastrointestinal Bleeding ◽  
Systematic Error ◽  
Observational Studies ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Causal Effect ◽  
Administrative Claims ◽  
Multiple Sources ◽  
True Effect Size ◽  
Healthcare Data ◽  
Negative Controls

Observational healthcare data, such as electronic health records and administrative claims, offer potential to estimate effects of medical products at scale. Observational studies have often been found to be nonreproducible, however, generating conflicting results even when using the same database to answer the same question. One source of discrepancies is error, both random caused by sampling variability and systematic (for example, because of confounding, selection bias, and measurement error). Only random error is typically quantified but converges to zero as databases become larger, whereas systematic error persists independent from sample size and therefore, increases in relative importance. Negative controls are exposure–outcome pairs, where one believes no causal effect exists; they can be used to detect multiple sources of systematic error, but interpreting their results is not always straightforward. Previously, we have shown that an empirical null distribution can be derived from a sample of negative controls and used to calibrate P values, accounting for both random and systematic error. Here, we extend this work to calibration of confidence intervals (CIs). CIs require positive controls, which we synthesize by modifying negative controls. We show that our CI calibration restores nominal characteristics, such as 95% coverage of the true effect size by the 95% CI. We furthermore show that CI calibration reduces disagreement in replications of two pairs of conflicting observational studies: one related to dabigatran, warfarin, and gastrointestinal bleeding and one related to selective serotonin reuptake inhibitors and upper gastrointestinal bleeding. We recommend CI calibration to improve reproducibility of observational studies.

scholarly journals Counterfactual Reasoning in Observational Studies

2019 ◽  
Vol 33 ◽  
pp. 9886-9887
Author(s):  
Negar Hassanpour
Keyword(s):  
Alternative Treatment ◽  
Observational Studies ◽  
Causal Effect ◽  
Intelligent Agent ◽  
Population Based ◽  
Causal Effects ◽  
Medical Procedure ◽  
Counterfactual Reasoning ◽  
Effect Estimation ◽  
The Individual

To identify the appropriate action to take, an intelligent agent must infer the causal effects of every possible action choices. A prominent example is precision medicine that attempts to identify which medical procedure will benefit each individual patient the most. This requires answering counterfactual questions such as: ""Would this patient have lived longer, had she received an alternative treatment?"". In my PhD, I attempt to explore ways to address the challenges associated with causal effect estimation; with a focus on devising methods that enhance performance according to the individual-based measures (as opposed to population-based measures).

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