Meta tags:
author= Gwern;
description= Compilation of studies comparing observational results with randomized experimental results on the same intervention, compiled from medicine/economics/psychology, indicating that a large fraction of the time (although probably not a majority) correlation ≠ causality.;
keywords= economics, insight-porn, psychology/cognitive-bias, statistics/bayes/regression-to-mean, statistics/bias, statistics/causality;
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how, often, does, correlation, causality, medical, economics, sociology, psychology, education, todo, backlinks, similar, links, bibliography,
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edictor values that equate studies at an ideal or a reasonable level the projections in table 6 assume that both randomized and nonrandomized experiments used passive control groups internal control groups and matching allowed exact computation of d had no attrition standardized treatments were published had pretest effect sizes of zero used n 1 000 subjects per study did not allow self selection of subjects into conditions and used outcomes based on self reports and specifically tailored to treatment area effects and interaction effects between area and assignment were included in the projection note that the overall difference among the eight cell means has diminished dramatically in comparison with table 2 in table 2 the lowest cell mean was 0 23 and the highest was 0 37 for a range of 0 60 the range in table 6 is only half as large 0 34 the same conclusion is true for the range within each area in table 2 that range was 0 01 for the smallest difference between randomized and nonrandomized experiments sat coaching to 0 21 for the largest difference drug use prevention in table 6 the range was 0 11 sat coaching 0 01 ability grouping 0 05 presurgical interventions and 0 09 drug use prevention put a bit more simply nonrandomized experiments are more like randomized experiments if one takes confounds into account comparison of evidence of treatment effects in randomized and nonrandomized studies ioannidis et al 2001 25ya study selection 45 diverse topics were identified for which both randomized trials n 240 and nonrandomized studies n 168 had been performed and had been considered in meta analyses of binary outcomes data extraction data on events per patient in each study arm and design and characteristics of each study considered in each meta analysis were extracted and synthesized separately for randomized and nonrandomized studies data synthesis very good correlation was observed between the summary odds ratios of randomized and nonrandomized studies r 0 75 p 001 however non randomized studies tended to show larger treatment effects 28 vs 11 p 0 009 between study heterogeneity was frequent among randomized trials alone 23 and very frequent among nonrandomized studies alone 41 the summary results of the 2 types of designs differed beyond chance in 7 cases 16 discrepancies beyond chance were less common when only prospective studies were considered 8 occasional differences in sample size and timing of publication were also noted between discrepant randomized and nonrandomized studies in 28 cases 62 the natural logarithm of the odds ratio differed by at least 50 and in 15 cases 33 the odds ratio varied at least 2 fold between nonrandomized studies and randomized trials conclusions despite good correlation between randomized trials and nonrandomized studies in particular prospective studies discrepancies beyond chance do occur and differences in estimated magnitude of treatment effect are very common comparison of effects in randomized controlled trials with observational studies in digestive surgery shikata et al 2006 20ya methods the pubmed 1966 60ya to april 2004 22ya embase 1986 40ya to april 2004 22ya and cochrane databases issue 2 2004 22ya were searched to identify meta analyses of randomized controlled trials in digestive surgery fifty two outcomes of 18 topics were identified from 276 original articles 96 randomized trials 180 observational studies and included in meta analyses all available binary data and study characteristics were extracted and combined separately for randomized and observational studies in each selected digestive surgical topic summary odds ratios or relative risks from randomized controlled trials were compared with observational studies using an equivalent calculation method results significant between study heterogeneity was seen more often among observational studies 5 of 12 topics than among randomized trials 1 of 9 topics in 4 of the 16 primary outcomes compared 10 of 52 total outcomes summary estimates of treatment effects showed significant discrepancies between the two designs conclusions one fourth of observational studies gave different results than randomized trials and between study heterogeneity was more common in observational studies in the field of digestive surgery a comparison of approaches to advertising measurement evidence from big field experiments at facebook gordon et al 2019 we examine how common techniques used to measure the causal impact of ad exposures on users conversion outcomes compare to the gold standard of a true experiment randomized controlled trial using data from 12 us advertising lift studies at facebook comprising 435 million user study observations and 1 4 billion total impressions we contrast the experimental results to those obtained from observational methods such as comparing exposed to unexposed users matching methods model based adjustments synthetic matched markets tests and before after tests we show that observational methods often fail to produce the same results as true experiments even after conditioning on information from thousands of behavioral variables and using non linear models we explain why this is the case our findings suggest that common approaches used to measure advertising effectiveness in industry fail to measure accurately the true effect of ads figure 13 summarizes results for the four studies for which there was a conversion pixel on a registration page figure 14 summarizes results for the three studies for which there was a conversion pixel on a key landing page the results for these studies vary across studies in how they compare to the rct results just as they do for the checkout conversion studies reported in figures 11 and 12 we summarize the performance of different observational approaches using two different metrics we want to know first how often an observational study fails to capture the truth said in a statistically precise way for how many of the studies do we reject the hypothesis that the lift of the observational method is equal to the rct lift table 7 reports the answer to this question we divide the table by outcome reported in the study checkout is in the top section of table 7 followed by registration and page view the first row of table 7 tells us that of the 11 studies that tracked checkout conversions we statistically reject the hypothesis that the exact matching estimate of lift equals the rct estimate as we go down the column the propensity score matching and regression adjustment approaches fare a little better but for all but one specification we reject equality with the rct estimate for half the studies or more we would also like to know how different the estimate produced by an observational method is from the rct estimate said more precisely we ask across evaluated studies of a given outcome what is the average absolute deviation in percentage points between the observational method estimate of lift and the rct lift for example the rct lift for study 1 checkout outcome is 33 the em lift estimate is 117 hence the absolute lift deviation is 84 percentage points for study 2 checkout outcome the rct lift is 0 9 the em lift estimate is 535 and the absolute lift deviation is 534 percentage points when we average over all studies exact matching leads to an average absolute lift deviation of 661 percentage points relative to an average rct lift of 57 across studies see the last two columns of the first row of the table measuring consumer sensitivity to audio advertising a field experiment on pandora internet radio huang et al 2018 how valuable is the variation generated by this experiment since it can be difficult to convince decision makers to run experiments on key economic decisions and it consumes engineering resources to implement such an experiment properly could we have done just as well by using observational data to investigate this question we re run our analysis using only the 17 million listeners in the control group since they were untouched by the experiment in the sense that they received the default ad load in the absence of experimental instrumental variables we run the regression based on naturally occurring variation in ad load such as that caused by higher advertiser demand for some listeners than others excluding listeners who got no ads during the experimental period the results are found in table 7 we find that the endogeneity of the realized ad load some people get more ad load due to advertiser demand and these people happen to listen less than people with lower advertiser demand causes us to overestimate the true causal impact of ad load by a factor of approximately 4 to give the panel estimator its best shot we use what we have learned from the experiment and allow the panel estimator a 20 month time period between observations we see from table 8 that the point estimate for active days is much closer to that found in table 5 but it still overestimates the impact of ad load by more than the width of our 95 confidence intervals the panel point estimate for total hours while an improvement over the cross sectional regression results still overestimates the effect by a factor of 3 our result suggests that even after controlling for time invariant listener heterogeneity observational techniques still suffer from omitted variable bias caused by unobservable terms that vary across individuals and time that correlate with ad load and listening behaviors and without a long run experiment we would not have known the relevant timescale to consider to measure the long run sensitivity to advertising which is what matters for the platform s policy decisions education an evaluation of bias in three measures of teacher quality value added classroom observations and student surveys bacher hicks et al 2017 validating teacher effects on students attitudes and behaviors evidence from random assignment of teachers to students blazar 2017 there is growing interest among researchers policymakers and practitioners in identifying teachers who are skilled at improving student outcomes beyond test scores however important questions remain about the validity of these teacher effect estimates leveraging the random assignment of teachers to classes i find that teachers have causal effects on their students self reported behavior in class self efficacy in math and happiness in class that are similar in magnitude to effects on math test scores weak correlations between teacher effects on different student outcomes indicate that these measures capture unique skills that teachers bring to the classroom teacher effects calculated in non experimental data are related to these same outcomes following random assignment revealing that they contain important information content on teachers however for some non experimental teacher effect estimates large and potentially important degrees of bias remain these results suggest that researchers and policymakers should proceed with caution when using these measures they likely are more appropriate for low stakes decisions such as matching teachers to professional development than for high stakes personnel decisions and accountability in table 5 i report estimates describing the relationship between non experimental teacher effects on student outcomes and these same measures following random assignment cells contain estimates from separate regression models where the dependent variable is the student attitude or behavior listed in each column the independent variable of interest is the non experimental teacher effect on this same outcome estimated in years prior to random assignment all models include fixed effects for randomization block to match the experimental design in order to increase the precision of my estimates models also control for students prior achievement in math and reading student demographic characteristics and classroom characteristics from randomly assigned rosters validity evidence for teacher effects on students math performance are consistent with other experimental studies kane et al 2013 13ya kane and staiger 2008 18ya where predicted differences in teacher effectiveness in observational data come close to actual differences following random assignment of teachers to classes the non experimental teacher effect estimate that comes closest to a 1 1 relationship is the shrunken estimate that controls for students prior achievement and other demographic characteristics 0 995 sd despite a relatively small sample of teachers the standard error for this estimate 0 084 is substantively smaller than those in other studies including the meta analysis conducted by bacher hicks et al 2017 and allows me to rule out relatively large degrees of bias in teacher effects calculated from this model a likely explanation for greater precision in this study relative to others is the fact that other studies generate estimates through instrumental variables estimation to calculate treatment on the treated instead i use ols regression and account for non compliance by narrowing in on randomization blocks in which very few in any students moved out of their randomly assigned teachers classroom non experimental teacher effects calculated without shrinkage are related less strongly to current student outcomes though differences in estimates and associated standard errors between panel a and panel b are not large all corresponding estimates eg model 1 from panel a versus panel b have overlapping 95 confidence intervals for both self efficacy in math and happiness in class non experimental teacher effect estimates have moderate predictive validity generally i can distinguish estimates from 0 sd indicating that they contain some information content on teachers the exception is shrunken estimates for self efficacy in math although estimates are similar in magnitude to the unshrunken estimates in panel a between 0 42 sd and 0 58 sd standard errors are large and 95 confidence intervals cross 0 sd i also can distinguish many estimates from 1 sd this indicates that non experimental teacher effects on students self efficacy in math and happiness in class contain potentially large and important degrees of bias for both measures of teacher effectiveness point estimates around 0 5 sd suggest that they contain roughly 50 bias table 5 relationship between current student outcomes and prior non experimental teacher effect outcomes 3 4 effects 1 imply bias todo lonn em yusuf s is there a role for antioxidant vitamins in the prevention of cardiovascular disease an update on epidemiological and clinical trials data can j cardiol 1997 29ya 13 957 965 patterson re white e kristal ar neuhouser ml potter jd vitamin supplement and cancer risk the epidemiological evidence cancer causes control 1997 29ya 8 786 802 comparisons of effect sizes derived from randomized and non randomized studies bc reeves rr maclehose im harvey ta sheldon health services research 1998 28ya superseded by m...
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