Questions tagged [treatment-effect]

A treatment effect is the causal effect of some "treatment" or policy intervention on an outcome variable. Such effects can be estimated with data from randomized or quasi experiments, and clinical trials or with observational data and methods for causal inference.

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Analysis Ideas for Unusual Observational Time Series w/Treatment

I am studying some retrospective time series survey (panel) data, sometimes with an intervening treatment, and would like feedback on an analysis concept to test for a treatment effect. Each ...
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Propensity score matching - What is the problem?

In estimation of treatment effects a commonly used method is matching. There are offcourse several techniques used for matching but one of the more popular techniques is propensity-score matching. ...
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Diff-in-Diff time varying treatment

I would like to estimate a Difference in Difference specification where the treatment (i.e., the policy change P) is off then on and then off again: ...
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56 views

Why are my coefficients too large when control variables are not added?

I am doing a difference in differences analysis with staggered treatment time. Since the treatment time is different among subjects, I made my matrices look something like this from this post (Dynamic ...
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Fixed effects with panel data vs including lagged variables with cross section data

I have panel data with many groups $i$ and two time periods $t$. I want to know the effect of a binary treatment $D$ on a continuous outcome $Y$. Some groups go from untreated to treated, while others ...
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Aggregation Estimation Issues

I'm analyzing the effect of a law enforcement measure over time on reported violence for districts in a city. If I consider the city as a whole I have access to a lot of possible control variables (e....
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Is there a joint test of model coefficients, effectively testing for main and interaction effects in quantile linear mixed model?

I am just reading this paper: Linear Quantile Mixed Models: The lqmm Packagefor Laplace Quantile Regression. Let us assume I have a repeated observation experiment, where I want to assess the effect ...
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33 views

What is the best way to model when the treatment is intermittent across years?

Let's say the goal is to gauge the effect of deploying certain clean technology (dummy variable: 1 for the year the technology is deployed, 0 otherwise) on a firm's pollution level (continuous ...
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37 views

Winsorizing propensity scores

Is it kosher? Inverse propensity weights (IPW) has been shown to perform poorly when selection probabilities are small (Kang and Schafer, 2007). Are there any standard solutions to this issue?
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Extending external validation diagnostics to experiments with continuous treatment

Does the external validation diagnostic methods discussed in Stuart et al. (2011) (i.e., inverse propensity score weighted regressions) also apply to the experimental setting in which the treatment is ...
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27 views

Cox proportional hazards model with inverse probability treatment weights: testing the Cox proportional hazards assumption

I have survival data of persons who participated/didn't participate in a health promotion program. To estimate the effect of the health promotion program on the hazard of mortality, I plan to use a ...
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Strong ignorability: confusion on the relationship between outcomes and treatment

In the research area of potential outcomes and individual treatment effect (ITE) estimation, a common assumption called ''strong ignorability'' is often made. Given a graphical model with the ...
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Estimating policy intervention effect for comparison groups but no control

I have panel data in 6 waves for land parcels that include 11 covariates that are known to affect the outcome of interest (probability of becoming a protected area). Six of the covariates are time-...
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Conditional treatment effects for RCTs

I have randomized controlled trial data with one control and two treatments. Since the DV is continuous, I estimate the treatment effect using an lm model, while ...
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How to compute the Quantile Treatment Effect?

Consider you have defined a statistical model for the potential outcomes $Y_i(0)$ and $Y_i(1)$ for each experimental unit $i=1,\cdots, N$, as in Rubin (1978)'s model-based inference for causal ...
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How is it decided to use Difference in Differences, Regression Discontinuity or both together (if possible) according to the policy change?

I am quite new at (quasi) natural experiment analysis, so please bear with me, and got some questions. In my research, I am planning to use one policy change which happened 2002 and treated firms are ...
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Correct test for before and after samples of binary counts?

TL;DR --- for $n = 1,\dots,N$ counties, we have the numbers $x_n$ and $y_n$ of STI+ and STI- people for two different years. Between year 1 and year 2, a small number $M$ of the counties closed ...
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compare time series of different treatments (with pre- posttest data)

I'm having a bit of trouble analyzing some time series for my research project. I have 6 plots in a field, and each plot has 2 water content sensors that measure every 30 minutes. I collected data ...
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Extracting Classifier for Data with Three Distinct Trends

I am investigating a dataset and have found the outcome has three, or maybe four, distinct groups of data with positive correlation. I would like to fit two lines to classify my data points by which ...
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Do I need to control for prognostic variables in a Cox PH model that estimates a treatment effect if the sample matches the target population?

I've been told that if all known prognostic factors are not adjusted for in a non linear model, in this case a Cox PH model, that because the error term is not estimated the treatment variable will ...
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76 views

How to model non-experimental continuous individual treatment effect on a binary target?

I have historical data of a problem that can be described as: A person represented by features X has to wait T minutes on a queue so she can receive a treatment (which is equal for everyone), and ...
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56 views

Average treatment effect , binary outcome and odds ratio

I am struggling with ATE. I have two treatment group in a cohort (binary 0=treatment A; 1=treatment B). I want to know the odds of variable Y (binary 0=no event; 1=event) b/w two treatment groups ...
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Use Target Metric or Related Metric for Intervention Analysis?

Say I have a target metric and a second metric related to the target, and I want to measure the effect of an intervention on the target metric. The intervention affects both metrics, but not always ...
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216 views

Statistical Analysis of fixed sequence crossover design-placebo followed by medicine

I am planning a fixed-sequence 2-treatment (placebo followed by drug) crossover study. There is a Placebo run-in period of 15 days. Placebo is the control and has no effect on BP. The aim is to ...
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Aggregate effects of randomized experiment

Consider the following two-equation model for observational data from a randomized experiment: (1) $y_{ij} = \alpha + \beta D_{ij} + \gamma X_{j} + \varepsilon_{ij}$ (2) $D_{ij} = \gamma + \delta ...
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Does the regression intercept capture the effect of the treatment year?

I read in a notes that runs the following regression $Y_{it}=\alpha+\beta_1D^{-1}_{it}+\beta_2D^{+1}_{it}+\gamma X_{it}+\epsilon_{it}$ $Y_{it}$ is some health index for individual $i$ in year $t$, $...
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Instrumental variables: In which cases would the average treatment effect on the treated (ATT) and local average treatment effect (LATE) be similar?

It seems that if the proportion of always-takers in the control group (to whom eligibility was not assigned) is much smaller than the proportion of compliers in the treatment group (to whom ...
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Why is the Tukey method not appropriate for covariance analysis when estimating treatment effects?

In the book 'Applied Linear Statistical Models' chapter 22 it states that the Tukey method is not appropriate for covariance analysis. The context is in estimation of treatment effects. Instead, when ...
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matching then fixed effects

I have an unbalanced panel over time. Agents are sometimes in the panel and sometimes not. The sample share of certain groups (e.g. females) varies a lot over time (little at the start, more at the ...
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Can I carry out a Propensity Score Matching with a general population of 90 observations and a treatment group of 20?

My population consists of 90 administrative zones that divide the city. Of those zones, only 20 received the treatment. After carrying out PSM, I have 17 zones in the treatment group and 17 in the ...
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Effect size between control and treatment group for likert scale questions

I am doing some surveys to study cognitive biases in energy related consumer behaviours. I have several questions that I asked to randomly assign control and treatment groups with different answer ...
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47 views

Guidelines for comparing treatments when some patients receive different number of treatments

I am working with a retrospective database where the researchers want to compare the effects between 2 treatments. Things get complicated with the fact that some of the patients receive treatments ...
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276 views

Difference in Differences with Multiple Time Periods and Multiple Treatment Periods

I'm working on a panel data difference in differences model where each unit (country) is observed every year. In any year, a country can be engaged in a war (treatment), a militarized interstate ...
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28 views

How to compare treatments in multitreatment setting of observational treatment study

I am researching uplift models to measure effect of treatment. Particularly when there are multiple treatments and I want to compare/order treatments based on their causal effect on average/individual....
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42 views

Difference-in-difference using tobit regression with mediation (controlling for fixed effects)

I want to conduct a mediation analysis in a difference-in-difference setting. I want to determine which element of the policy was effective. My difference-in-difference is based on a matched sample ...
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1answer
36 views

Effect of treatment in a model with random effects

I am working with Eyes (volume), each person has two eyes and I am using random effects to account for this in my model. (linear mixed effects model) The problem with nlme is that the output needs ...
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21 views

Panel data treatment effects with multiple treatments

I have a fairly large balanced panel dataset (1000 N by 200 T) that I want to estimate a treatment effect for. My first thought was a DID framework on this, but I'm unsure how to proceed because the ...
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49 views

Causal inference using regression for multiple covariates

I am reading lot of material regarding Causal Inference using Regression Analysis but I am unable to resolve my doubt. Suppose I have a data with Outcome Y, Treatment Tr and covariates X1, X2, X3, X4,...
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131 views

How to interpret event study/diff in diff specification with continuous treatment?

Say you are running a diff in diff, where some treatment occured nationally at time t=0, and you use some continuous measure of pre-existing characteristics (or some continuous instrument) to capture '...
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Is there something like Rosenbaum's Gamma (sensitivity to unobserved bias) for the case where the bias reduces the effect/increases p-value?

I am doing some observational studies using matching methods (propensity score, direct matching, etc...). I am interested in looking at the sensitivity of my results to unobserved bias. I read about ...
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249 views

Panel data diff-in-diff and the pattern of the binary treatment indicator

I am self-studying the book Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge and stumped upon a question while reading the chapter on Advance Panel Data Methods (Ch. 14). Normally,...
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63 views

Why don't people report the accuracy, ppv, or npv of their propensity score models

I'm using propensity score matching to estimate causal treatment effects. I have been concerned about diagnostic metrics for my propensity score model. However... when I look at the literature, no ...
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84 views

Does a balanced design have to be connected?

Under a general block design setup, consider $v$ treatments allocated to $b$ blocks. Let $r_i$ and $k_j$ be the replication number of the $i$th treatment and $j$ the block respectively. We define $n_{...
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What test to use for population proportion for a control/treatment setup?

I am not that used to searching the right test statistic, however, I have some basic knowledge about hypothese testing. My question is if there is a name/standart procedure in R for testing the ...
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need help understanding propensity score matching (what is my treatment vs my outcome)

suppose I want to incorporate propensity score matching in analyzing sales. Last year, I sold 100 of 300, so my ratio is 33.33%. This year my items costs 5% less and I sold 300/600, so my ratio is ...
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37 views

Experimental Design---Definition of treatment

I am learning about Experimental Design and I am confused with an example: 'The following chart displays the burning times of flares of two different type of torch design. The engineers are ...
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20 views

IV-assumptions with constant treatment effects vs. heterogeneous treatment effects

I am reading chapter 4 about IV-estimation in the book Mostly Harmless Econometrics by Angrist & Pischke (2009). In the case of heterogenous treatment effects, I understand the identification and ...
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How to analyse this pre-test and post-test double-blinded RCT study? [duplicate]

I have conducted a study where participants are split between two groups (treatment and control). I want to measure changes in depression and sleep disturbance pre and post-test (before and after ...
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64 views

Proper Average Treatment Effect estimators (and standard errors) for Generalized Linear Models with log link?

What is the proper formula for estimating the Average Treatment Effect with a simple main effects generalized linear model? My first pass at defining the Average Treatment Effect for a GLM with a log ...
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Why do estimates differ so much between Instrumental Variable (ivreg) vs 2SLS (lm)?

I am working with some messy pilot data to figure out whether Instrumental Variable analysis will help interpret the results of a randomized controlled trial I am preparing. Treatment compliance in ...

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