# 统计作业代写Statistics代考|Establishing Causal Associations

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• Date Analysis数据分析
• Actuarial Science 精算科学
• Bayesian Statistics 贝叶斯统计
• Generalized Linear Model 广义线性模型
• Macroeconomic statistics 宏观统计学
• Microeconomic statistics 微观统计学
• Logistic regression 逻辑回归
• linear regression 线性回归

## 统计作业代写STATISTICS代考|putative effect

Involves causal inference: establishing that one variable produces or causes another. Causality is a challenging topic because, to ascertain its existence with observational (nonexperimental) data, we should consider all potential confounding variables that might explain why two variables are associated. Yet, few studies have the resources necessary to measure the myriad factors that might account for an association (remember Chapter 12’s discussion of omitted variable bias?). Nevertheless, statisticians have developed some promising tools that move us closer to the ability to make causal claims, even with observational data. Two related methods that are growing in popularity are called propensity score matching and weighting. These methods are simpler to understand if we imagine two groups: the treatment group that is exposed to the presumed cause and the control group that is not exposed to the presumed cause. In a true experiment, researchers may control who or what is and is not exposed to the cause and then observe

the putative effect. For example, an experimental vaccine designed to target the coronavirus is given to a randomly chosen sample of 100 people and a placebo to another randomly chosen sample of 100 people. Researchers then compare who does and does not experience COVID-19 symptoms during follow-up, which might be based on a regression model, a simple test of proportions, or some other statistical approach. Because of randomization of participants into the experimental and control groups, in all likelihood the systematic differences between the two groups are controlled or partialled out, so, if the treatment group does not experience symptoms (or they are greatly reduced), the vaccine is, with high probability, the cause of COVID-19 inhibition.

But in observational research projects-such as those represented by the datasets used in the previous chapters-researchers rarely have this level of control over the assignment of the presumed cause, so they must take a different approach and attempt to account for differences between groups in the sample. In an LRM, analysts statistically adjust for differences by including potential confounding variables, which might account for all differences across individuals and allow them to isolate the “cause” of some outcome. However, a more likely scenario is that important variables are left out of the model because they are not available in the dataset or perhaps the conceptual model guiding the research is not developed enough. Using LRMs to make causal inferences also requires other stringent assumptions. ${ }^{5}$ Of course, all of this assumes that there is a single “cause” of an outcome and the goal is to identify it. This is often a fair assumption in the medical sciences or program evaluation when one wishes to know whether, say, marijuana vaping causes mouth cancer or a school nutrition program triggers more vegetable consumption. But when studying social and behavioral phenomena many outcomes are multi-causal or may involve complex interactions and nonlinear associations that require a highly developed conceptual model to identify. Thus, the focus on identifying single cause-and-effect relationships is often myopic. Rather than using LRMs in an attempt to identify a single causal factor, it might be preferable in many instances to be guided by a conceptual model that proposes that multiple factors affect an outcome. ${ }^{6}$

## matlab代写

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.