You Can’t Drive a Car With Only Three Wheels

2019 ◽  
Vol 188 (9) ◽  
pp. 1682-1685 ◽  
Author(s):  
Hailey R Banack
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Causal Effects ◽  
Measurement Bias ◽  
Exposure Misclassification ◽  
Unmeasured Confounding ◽  
Obstetrical Care ◽  
The Impact ◽  
Fundamental Requirement

Abstract Authors aiming to estimate causal effects from observational data frequently discuss 3 fundamental identifiability assumptions for causal inference: exchangeability, consistency, and positivity. However, too often, studies fail to acknowledge the importance of measurement bias in causal inference. In the presence of measurement bias, the aforementioned identifiability conditions are not sufficient to estimate a causal effect. The most fundamental requirement for estimating a causal effect is knowing who is truly exposed and unexposed. In this issue of the Journal, Caniglia et al. (Am J Epidemiol. 2019;000(00):000–000) present a thorough discussion of methodological challenges when estimating causal effects in the context of research on distance to obstetrical care. Their article highlights empirical strategies for examining nonexchangeability due to unmeasured confounding and selection bias and potential violations of the consistency assumption. In addition to the important considerations outlined by Caniglia et al., authors interested in estimating causal effects from observational data should also consider implementing quantitative strategies to examine the impact of misclassification. The objective of this commentary is to emphasize that you can’t drive a car with only three wheels, and you also cannot estimate a causal effect in the presence of exposure misclassification bias.

scholarly journals Adversarial balancing-based representation learning for causal effect inference with observational data

2021 ◽  
Author(s):  
Xin Du ◽  
Lei Sun ◽  
Wouter Duivesteijn ◽  
Alexander Nikolaev ◽  
Mykola Pechenizkiy
Keyword(s):  
Health Care ◽  
Observational Data ◽  
Causal Effect ◽  
Representation Learning ◽  
Average Treatment Effect ◽  
Treatment Selection ◽  
Causal Effects ◽  
Control Group ◽  
Confounding Bias ◽  
The Impact

AbstractLearning causal effects from observational data greatly benefits a variety of domains such as health care, education, and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist clinical planning and improve the survival rate. In this paper, we focus on studying the problem of estimating the Conditional Average Treatment Effect (CATE) from observational data. The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, in the presence of confounding bias; on the other hand, we have to deal with the identification of the CATE when the distributions of covariates over the treatment group units and the control units are imbalanced. To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on recent advances in representation learning. To ensure the identification of the CATE, ABCEI uses adversarial learning to balance the distributions of covariates in the treatment and the control group in the latent representation space, without any assumptions on the form of the treatment selection/assignment function. In addition, during the representation learning and balancing process, highly predictive information from the original covariate space might be lost. ABCEI can tackle this information loss problem by preserving useful information for predicting causal effects under the regularization of a mutual information estimator. The experimental results show that ABCEI is robust against treatment selection bias, and matches/outperforms the state-of-the-art approaches. Our experiments show promising results on several datasets, encompassing several health care (and other) domains.

A Survey on Causal Inference

2021 ◽  
Vol 15 (5) ◽  
pp. 1-46
Author(s):  
Liuyi Yao ◽  
Zhixuan Chu ◽  
Sheng Li ◽  
Yaliang Li ◽  
Jing Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Research Direction ◽  
Estimation Methods ◽  
Potential Outcome ◽  
Outcome Framework ◽  
Benchmark Datasets ◽  
Inference Methods

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

Causal inference via string diagram surgery

2021 ◽  
pp. 1-22
Author(s):  
Bart Jacobs ◽  
Aleks Kissinger ◽  
Fabio Zanasi
Keyword(s):  
Causal Inference ◽  
Probabilistic Reasoning ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Causal Effects ◽  
Stochastic Matrices ◽  
Counterfactual Reasoning ◽  
Special Cases ◽  
String Diagram ◽  
Set Up

Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.

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.

Causal inference for clinicians

2019 ◽  
Vol 24 (3) ◽  
pp. 109-112 ◽  
Author(s):  
Steven D Stovitz ◽  
Ian Shrier
Keyword(s):  
Decision Making ◽  
Causal Inference ◽  
Clinical Decision Making ◽  
Causal Effect ◽  
Treatment Decision ◽  
Clinical Decision ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Treatment Decision Making ◽  
Inference Methods

Evidence-based medicine (EBM) calls on clinicians to incorporate the ‘best available evidence’ into clinical decision-making. For decisions regarding treatment, the best evidence is that which determines the causal effect of treatments on the clinical outcomes of interest. Unfortunately, research often provides evidence where associations are not due to cause-and-effect, but rather due to non-causal reasons. These non-causal associations may provide valid evidence for diagnosis or prognosis, but biased evidence for treatment effects. Causal inference aims to determine when we can infer that associations are or are not due to causal effects. Since recommending treatments that do not have beneficial causal effects will not improve health, causal inference can advance the practice of EBM. The purpose of this article is to familiarise clinicians with some of the concepts and terminology that are being used in the field of causal inference, including graphical diagrams known as ‘causal directed acyclic graphs’. In order to demonstrate some of the links between causal inference methods and clinical treatment decision-making, we use a clinical vignette of assessing treatments to lower cardiovascular risk. As the field of causal inference advances, clinicians familiar with the methods and terminology will be able to improve their adherence to the principles of EBM by distinguishing causal effects of treatment from results due to non-causal associations that may be a source of bias.

How Much Is Minnesota Like Wisconsin? Assumptions and Counterfactuals in Causal Inference with Observational Data

2013 ◽  
Vol 21 (2) ◽  
pp. 193-216 ◽  
Author(s):  
Luke Keele ◽  
William Minozzi
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Causal Effects ◽  
Empirical Literature ◽  
Careful Evaluation ◽  
Research Designs

Political scientists are often interested in estimating causal effects. Identification of causal estimates with observational data invariably requires strong untestable assumptions. Here, we outline a number of the assumptions used in the extant empirical literature. We argue that these assumptions require careful evaluation within the context of specific applications. To that end, we present an empirical case study on the effect of Election Day Registration (EDR) on turnout. We show how different identification assumptions lead to different answers, and that many of the standard assumptions used are implausible. Specifically, we show that EDR likely had negligible effects in the states of Minnesota and Wisconsin. We conclude with an argument for stronger research designs.

scholarly journals Causal inference under over-simplified longitudinal causal models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lola Étiévant ◽  
Vivian Viallon
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Repeated Measurements ◽  
Causal Models ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Time Varying ◽  
Weighted Averages

Abstract Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether – and how – causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted averages of longitudinal causal effects of interest. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that the quantities estimated in practice should be interpreted with caution in general, as they usually do not relate to any longitudinal causal effect of interest. Our simulations further illustrate that the bias between the quantities estimated in practice and the weighted averages of longitudinal causal effects of interest can be substantial. Overall, our results confirm the need for repeated measurements to conduct proper analyses and/or the development of sensitivity analyses when they are not available.

scholarly journals Shut Down Schools, Knock Down the Virus? No Causal Effect of School Closures on the Spread of COVID-19

2021 ◽  
Author(s):  
Kentaro Fukumoto ◽  
Charles T. McClean ◽  
Kuninori Nakagawa
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Causal Effects ◽  
Negative Consequences ◽  
School Closures ◽  
Knock Down ◽  
Level Data ◽  
Children And Parents ◽  
Municipal Level ◽  
Do So

AbstractAs COVID-19 spread in 2020, most countries shut down schools in the hopes of slowing the pandemic. Yet, studies have not reached a consensus about the effectiveness of these policies partly because they lack rigorous causal inference. Our study aims to estimate the causal effects of school closures on the number of confirmed cases. To do so, we apply matching methods to municipal-level data in Japan. We do not find that school closures caused a reduction in the spread of the coronavirus. Our results suggest that policies on school closures should be reexamined given the potential negative consequences for children and parents.

scholarly journals Interpretation of mendelian randomization using one measure of an exposure that varies over time.

2021 ◽  
Author(s):  
Tim T Morris ◽  
Jon Heron ◽  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Kate Tilling
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Observational Data ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Time Points ◽  
The Life Course ◽  
Over Time

Background Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g., weight measured once between ages 40 and 60). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time. Methods We propose an approach that emphasises the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example. Results We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the liability as induced by a specific genotype that gives rise to the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the liability of exposure is constant over time), as we demonstrate by estimating the effect of BMI measured at different ages on systolic blood pressure. Conclusions Practitioners should not interpret MR results as timepoint-specific direct or total causal effects, but as the effect of changing the liability that causes the entire exposure trajectory. Estimates of how the effects of a genetic variant on an exposure vary over time are needed to interpret timepoint-specific causal effects.

scholarly journals Polynomial Mendelian Randomization reveals widespread non-linear causal effects in the UK Biobank

2021 ◽  
Author(s):  
Jonathan Sulc ◽  
Jenny Sjaarda ◽  
Zoltan Kutalik
Keyword(s):  
Causal Inference ◽  
Large Scale ◽  
Causal Effect ◽  
Causal Effects ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Individual Level ◽  
Glucose Levels ◽  
Causal Function ◽  
The Uk

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

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