随机控制实验了解接受远端祈祷心脏病病人的治疗效果课件

按一下以編輯母片標題樣式,按一下以編輯母片,第二層,第三層,第四層,第五層,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,*,Questions,The relationship of interest,The ideal experiment,The identification strategy,The mode of inference,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,1,QuestionsThe relationship of i,The relation of interest,Most questions are about casual and effect,Example,Smaller classes are better for the learning of the students,Lower copayment encourages the health utilizations,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,2,The relation of interestMost q,Coefficient Equations,Slope,y,-intercept,Prediction Equation,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,3,Coefficient EquationsSlopey-in,The estimates of regression shows the extent of“correlation”,but we are interested to know“causation”,Key issue in empirical analysis is separating,causation,from,correlation,.,Correlated,means that two economic variables move together.,Casual,means that one of the variables is causing the movement in the other.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,4,The estimates of regression sh,THE IMPORTANT DISTINCTION BETWEEN CORRELATION AND CAUSATION,There are many examples where causation and correlation get confused.,It is critical for government policy to understand the difference;otherwise policy may not have the intended impact.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,5,THE IMPORTANT DISTINCTION BETW,THE IMPORTANT DISTINCTION BETWEEN CORRELATION AND CAUSATION,One example concerns SAT preparation courses.,In 1988,Harvard interviewed its freshmen and found those who took SAT“coaching”courses scored 63 points lower than those who did not.,One dean concluded that the SAT courses were unhelpful and“the coaching industry is playing on parental anxiety.”,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,6,THE IMPORTANT DISTINCTION BETW,The Problem,In both examples,there is a common problem:an attempt to interpret a,correlation,as a,causal relationship,without sufficient thought to the underlying data generating process.,For any correlation between two variables A and B,there are three possible explanations for a correlation:,A is causing B.,B is causing A.,Some other factor is causing both.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,7,The ProblemIn both examples,t,The Problem,In the Harvard SAT example,the possibilities could be:,SAT prep courses worsen preparation for the SATs.,Those with poorer test taking ability take prep courses to try to catch up.,Those who are generally nervous both like to take prep courses and do the worst on standardized exams.,Harvard dean thought the first possibility was correct.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,8,The ProblemIn the Harvard SAT,The Problem,Although the peasants or the Harvard dean,could,actually be correct,odds are they are misinterpreting the underlying process at work.,For policy purposes,what we care about is causation.,Knowing that two factors are correlated gives you no predictive power.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,9,The ProblemAlthough the peasan,The Problem of Bias,In this case,the assignment of the intervention,was not random,.,This means the treatment and control groups are not identical.,Non-random assignment,in turn,could cause,bias,.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,10,The Problem of BiasIn this cas,The Problem of Bias,Bias,represents any source of difference between treatment and control groups that is correlated with the treatment,but,not,due to the treatment.,In the SAT example,the impact of SAT courses is biased by the fact that those who take the prep course are likely to do worse on the SAT for other reasons.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,11,The Problem of BiasBias repre,The Problem of Bias,By definition,such differences do,not,exist in a randomized trial,since the groups are,not,different in any consistent fashion.,As a result,randomized trials have no bias,and it is for this reason they are the“gold standard”for empirically estimating causal effects.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,12,The Problem of BiasBy definiti,The ideal experiment,What sort of experiment can ideally be used to capture the casual effect?,Golden standard:controlled trials,Randomize subjects into the treatment and control group,then compare their outcome difference between controlled and treatment group,Commonly used to answer questions in natural science,but difficult to implement to answer questions in social science for various issues,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,13,The ideal experimentWhat sort,MEASURING CAUSATION WITH DATA WED LIKE TO HAVE:RANDOMIZED TRIALS,With random assignment,the assignment of the intervention is not determined by anything about the subjects.,As a result,the treatment group is identical to the control group in every facet but one:the treatment group gets the intervention.,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,14,MEASURING CAUSATION WITH DATA,Example I:does the payer work?,Example(Harris et al.):“隨機控制實驗：瞭解接受遠端祈禱心臟病病人的治療效果“,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,15,Example I:does the payer work,Harris et el.,實驗設計原則：“隨機,控制,雙盲,事前，同時實驗.“,隨機：病人隨機分配到禱告與否,控制：有些病人沒有禱告,雙盲：病人或醫師不知道為實驗或對照組,事前：在治療前隨機分配,同時：實驗同時進行,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,16,Harris et el.實驗設計原則：“隨機,控制,雙,Harris et el.的設計,随机控制实验了解接受远端祈祷心脏病病人的治疗效果,17,Harri