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关于变量问题，列举几篇文章，若有需要可以进一步搜索。1.什么时候应该使用回归分析？控制变量意味着什么？2.如何选择正确的因变量(控制变量)，让你的计量模型不再肮脏，3.调节变量, 中介变量和控制变量啥区别与联系? 4.控制、调节和中介变量，系说，5.核心解释变量A不显著, 但加入变量B后, 为什么A和B都显著了？6.被解释变量比解释变量的层级更高的模型设定合理么？7.审稿: 协变量何时重要? 哪个重要, 有多重要？8.三张图秒懂, 混淆, 中介, 调节, 对撞, 暴露, 结果和协变量的复杂关系，9.因果推断专题：6.再谈混淆变量，10.什么时候需要标准化回归模型中的变量？11.因果推断专题：1.混淆变量，12.虚拟变量回归模型是什么? 政策评估的前件，13.11种与机器学习相关的多元变量分析方法汇总，14.回归中各变量的数值相差过大有事, 又有什么问题？
现在，越来越讲究因果推断识别，因此一个被称之为“条件独立性假设（CIA）”显得格外重要。今天，引荐一个关于控制变量选择的议题，即何为不好的（好的）控制变量？而不好的控制变量最好不要引入到回归模型中来，因为会造成习以为常的“选择性偏误问题”。
那到底什么是不好的控制变量呢？直接点讲，它指的是会受到解释变量影响的变量，即这些控制变量并没有在解释变量受到影响之前就已经是前置决定了的(predetermined)。比如，研究学历对收入的影响，那是不是需要控制一下职业呢？一旦控制职业变量，意味着是在同一职业里对学历影响收入进行研究，但这样做存在选择性偏误。学历会同时影响一个人的职业和收入的，即这里的控制变量职业并不是一个相对于解释变量——学历的前置变量，因此，可以认定它是一个不好的控制变量。如果觉得中文读起来不顺畅的话，可以看看《Mostly Harmless Econometrics: An Empiricist‘s Companion》里的一个chapter，原文如下：We have made the point that control for covariates can make the conditional independence assumption more plausible. But more control is not always better. Some variables are bad controls and should not be included in a regression model even when their inclusion might be expected to change the short regression coe¢ cients. Bad controls are variables that are themselves outcome variables in the notional experiment at hand. That is, bad controls might just as well be dependent variables too. Good controls are variables that we can think of as having been fixed at the time the regressor of interest was determined.The essence of the bad control problem is a version of selection bias, albeit somewhat more subtle than the selection bias. To illustrate, suppose we are interested in the effects of a college degree on earnings and that people can work in one of two occupations, white collar and blue collar. A college degree clearly opens the door to higher-paying white collar jobs. Should occupation therefore be seen as an omitted variable in a regression of wages on schooling? After all, occupation is highly correlated with both education and pay. Perhaps it's best to look at the e¤ect of college on wages for those within an occupation, say white collar only. The problem with this argument is that once we acknowledge the fact that college a¤ects occupation, comparisons of wages by college degree status within an occupation are no longer apples-to-apples, even if college degree completion is randomly assigned.
be outcomes in the causal nexus. In many cases, however, the timing is uncertain or unknown. In such cases, clear reasoning about causal channels requires explicit assumptions about what happened first, or the assertion that none of the control variables are themselves caused by the regressor of interest.
关于一些计量方法的合辑，参看①“实证研究中用到的200篇文章, 社科学者常备toolkit”、②实证文章写作常用到的50篇名家经验帖, 学者必读系列、③过去10年AER上关于中国主题的Articles专辑、④AEA公布2017-19年度最受关注的十大研究话题, 给你的选题方向，⑤2020年中文Top期刊重点选题方向, 写论文就写这些。后面，咱们又引荐了①使用CFPS, CHFS, CHNS数据实证研究的精选文章专辑！，②这40个微观数据库够你博士毕业了, 反正凭着这些库成了教授，③Python, Stata, R软件史上最全快捷键合辑！，④关于(模糊)断点回归设计的100篇精选Articles专辑！，⑤关于双重差分法DID的32篇精选Articles专辑！，⑥关于合成控制法SCM的33篇精选Articles专辑！⑦最近80篇关于中国国际贸易领域papers合辑！，⑧最近70篇关于中国环境生态的经济学papers合辑！⑨使用CEPS, CHARLS, CGSS, CLHLS数据库实证研究的精选文章专辑！⑩最近50篇使用系统GMM开展实证研究的papers合辑！
下面这些短链接文章属于合集，可以收藏起来阅读，不然以后都找不到了。