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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How to analyse this pre-test and post-test double-blinded RCT study?

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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What statistical method to use to compare only two (of multiple) groups with respect to a binary outcome?

$n$ patients are randomly given treatment $A$ or treatment $B$ (with 50% of patients receiving each). Some patients from either group may then choose to ignore the treatment (i.e. a third option $C$). ...
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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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Do specification differences make a difference in difference-in-differences?

My question relates to the following post: How do I interpret a "difference-in-differences" model with continuous treatment?. I am reproducing an equation from Acemoglu, Autor and Lyle (...
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What are the sensitivity analyses for propensity score matching-based estimation?

I'm interested in using Propensity Score Matching (PSM) to create matched control vs treatment sample and estimate the treatment effect. But the problem with PSM is that the sample is matched based on ...
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Multi-arm RCT Sample Size External and Internal valid

I am trying to figure out the sample size for a multi-arm Randomized Control Trial (non-clinical) that will have internal validity and representative of the population, say; population X Any ...
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How to calculate the observed absolute treatment effect?

I'm reading this article. The authors indicated that for a random sample of 12 characteristics and 3600 patients affected in tow arms (control and treatment arm), they fitted some treatment effect ...
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How to specify a risk model?

I'm reading this article. The authors indicated that for a random sample of 12 characteristics and 3600 patients affected in two arms (control and treatment arm), they fitted a risk model consisting ...
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Experimentation Time series - Find a control group whose mean follows closely the test group

I'm conducting an analysis from Jan to Dec where my test/treatment group is the combination of 5 stores with sales per month. The main objective is to find a control group of 10 stores over 150 ...
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113 views

What's the interpretation of the ratio of the odds ratio between the experimental and the control arm?

Table 1 of this article show the associations between the outcome and variables, the authors presented the OR_ctr in control arm and ...
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Kernel Matching

I wish to estimate a treatment effect using Kernel Matching, but I'm confused about the process. From a high level, Is A or B correct? Or are both considered Kernel matching? A (1) Estimate ...
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How to measure Sales lift for a campaign without experimental design?

I want to be able to calculate Sales Lift for a campaign conducted but prior to campaign no control group was established. So, I cannot measure the impact on Sales because of campaign to treatment ...
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How to prove that selection on observable

implies that the potential outcomes are independent of the treatment status conditional on the propensity score? Consider the tuple $\left ( y_{1}, y_{0}, w \right )$, where $\left ( y_{1}, y_{0}\...
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Kernel Estimation to Estimate Treatment Effect

I am trying to determine whether an estimator I came up with is just a non-parametric kernel estimator. I am performing a simulation study to estimate a treatment effect that I impose on my data. My ...
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How to prove that one-sided non-compliance implies $E\left ( y|z=0 \right )=E\left ( y_{0}|z=0 \right )$?

Let $d \in \left \{ 0,1 \right \}$ denote the treatment status with unity indicating that the individual has been treated and let $z \in \left \{ 0,1 \right \}$ denote a binary indicator for whether ...
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Estimating treatment effect using propensity score matching

Suppose that we are estimating a treatment effect using propensity score matching. We assume selection-on-observable. What is an additional assumption such that ATE (average treatment effect) equals ...
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Understanding average treatment effect estimator notation

I want to check that I understand the notation for the average treatment effect (ATE) estimator correctly, and hopefully some of you can double check this. I often try to understand formulas through ...
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How can I calculate the power of my analysis with binary response data?

I have three groups with ~ 15 individuals per group. Group 1 gets a treatment Group 2 is a control in the same area Group 3 is a further control in a different area The response is binary, 1 or ...
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Run OLS with multicollinearity

I apologize for my naive question. In this famous experiment they collected responses in 4 different groups: Control (no rewards provided) Gift reward (10 pieces of candy or 50 pieces of candy) ...
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Advice needed with hypothesis testing of experiment with crossover design

I have conducted a randomized, single-blinded experiment, stimulation vs sham, two-session, within-subject, cross-over design. In each of the experimental sessions, participants alternately received ...
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92 views

How is the standard error used in calculating a Tukey HSD defined and calculated?

I am looking at someone else’s published data where they report treatment means and then a single standard error value across treatment means. I want to know if certain treatments are significantly ...
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Defining Average treatment effect (ATE) & Average treatment effect on the treated (ATT)

I have some questions regarding the following theoretical model and finding the average treatment effect (ATE) and average treatment effect of the treated (ATT). I'm not sure if I'm defining them ...
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Applying Heterogeneous treatment effects to clinical research (non technical explanation)

I'm trying to understand the hype around this estimation of heterogeneous treatment effects in the machine learning literature lately. It seems super interesting, but alot of it is beyond me. I read ...
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Treatment interference (causal analysis)

I am doing research on students and their perception of their grades. Specifically, I want to do an experiment where students either (a) see their actual grades in a course (as a percentage) - the ...
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How to compare different causal inference methodologies for estimating Average Treatment Effect when true treatment effect is unknown?

I'm comparing various methods for estimating average treatment effects (ATEs) for cost savings in a case-control study on health insurance episode of care data for my employer. My company currently ...
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Alternatives to balance tables to examine selection bias

I was wondering if someone has ideas to statistically examine selection into programme participation? For example, would it make sense to present the results of a propensity score analysis? Thank you
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Causal inference for additive multiple treatments

I encountered a causal inference problem in practice and want to find if there is a previously established statistical toolset that can be applied to my problem. My problem is characterized as ...
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Significance of treatment effect when interactions are present

I am conducting an experiment in which I am testing the effect of some treatment, $X$, on some result $Y$. Every subject in both the treatment and control group is being measured multiple times (at ...
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Identifying treatments with non-zero effects

I have a complete block design experiment with a large number of treatments were I am trying to identify treatments with significant effects (positive or negative). I know the Friedman/Durbin tests ...
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Treatment variable not in control data set-how to make a dataframe suitable for regression?

I would appreciate some feedback on this particular problem I'm stuck at. Experiment: Between Subjects; Treatment Vs Control. 10 subjects per condition. The subjects are measured over 10 trails. (so ...
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How to incorporate the customers leaving (addition of new ones) in the measurement of treatment effectiveness?

Consider I have taken a random sample for forming control group in month of January with sample size 5k and treatment group size being 95k. I am Sending offers to treatment group which are intend to ...
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Regression: Potential Ouctomes Frameworkw with Heterogeneous Treatment Effect

This question came up as part of the practice problems in the Econometrics course I am taking. Its is the following. In the potential outcomes framework with heterogeneous (non-constant) treatment ...
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Not including main effect term in subgroup analysis?

I know there are plenty of previous Cross Validated posts regarding if one must include the main effect term if the interaction term is included. The general consensus is: no if you have a very good ...
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47 views

Mixed models in repeated measurement with one treatment

I have an experiment that includes 8 subjects during one treatment, measuring response variable. The hypothesis is that there is some correlation between AV and lactate during the treatment. Some ...
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Treatment effect in a balanced randomized experiment

In an ideal balanced randomized experiment where every profile's propensity score is 0.5 (under binary treatment), regardless of the value of the profile, the best we can know of the treatment effect(...
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31 views

Checking if treatment is effective without control group

I am experimenting with few treatments to see which one is effective. I do not have luxury of control group to compare against. In such scenario, is there any method to see if the treatment has been ...
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Predicted individual treatment effect with continous treatments

I'm trying to apply Rubin's counterfactual model in an observational setting using machine learning predictions to simulate the unseen treatment-outcome pairs, according to https://www.ncbi.nlm.nih....
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Path Model with (endogenous) Treatment - which model?

I have collected data on the intention to create a new business of my students. I measured it before the course (t1) and after it (t2). I have data on their absenteeism in class and the time they ...
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How are the Average treatment effects using Causal Trees (CT) and Causal Forest (CF) decision-trees estimated?

I have read the following paper: https://iopscience.iop.org/article/10.1088/1748-9326/aafa8f/pdf . The paper seem to be understandable, however, several questions I have got about causal trees. As I ...
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Is this a well-known source of small-sample bias?

Suppose we have an outcome $Y$ which is caused by a binary treatment $T$ and some continuous covariate $X$, such that: $$E[Y\mid X,T=1] = E[Y^1\mid X] = \beta_0 + \delta + \beta_X X$$ $$E[Y\mid X,T=...
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113 views

Formula for E(MSE) from Recursive Partitioning for Heterogeneous Causal Effects (Athey and Imben 2015)

I'm reading Recursive Partitioning for Heterogeneous Causal Effects (Athey and Imben 2015) and I'm confused about the formula for $\hat E[\mu(x;\Pi)]$ on page 8. The formula offered in the paper is ...
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How to handle studies that don’t provide an estimate of effect size in a meta-analysis?

Say we want to survey the literature to find out the effect of Treatment A on Value B. Some studies provide an effect size estimate with confidence intervals and t-test results (e.g., “Treatment A ...
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Bias in average treatment effect with least-squares when continous interaction terms

I hope someone can clarify this for me: When I am estimating treatment effects with a regression model, I can use interactions between the treatment indicator $W_i$ and covariates $X_i$ to model ...
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113 views

Difference-in-difference to estimate gradual (i.e. slow) policy effect

I want to evaluate the impact of a policy on a variable $Y$. In the figure, I plot the mean of $Y$ over time in both the control and treatment groups (the vertical line represents the reform). The ...
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Powering versus statistical significance in clinical trial design

I think I have a simple question that I have not seen directly answered elsewhere, and as a stats beginner, I'm wary to translate answers that are not directly answering this question - apologies if ...
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Expectation of potential outcomes formula

In Mostly Harmless Econometrics, the author uses the following identity to derive an estimator for the causal effect: $$E \left[ \frac{Y_i D_i} {p(X_i)} \right] = E \left[Y_{1i} \right]$$ where: $...
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Multiple Imputation with categorical variable for treatment: Do I have to impute stratified by treatment?

I want to impute data for a clinical trial with four treatments and analyze the data to determine if there is a treatment effect. Normally I would perform the imputation stratified by treatment so ...
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Assess temporary effect of treatment

Imagine that I have a treatment that reduces the likelihood of response to a stimulus. This could be anything you like, but the simplest example is of a treatment (e.g., hand washing, mask wearing, ...
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Before/After unpaired study with control villages analysed in R

For the implementation of a project, we have been following household access to services in two groups of villages, one an intervention group (I) and one a control group (C). Access as a Yes/No ...
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Treatment effect on the non treated if there are no never-takers and no defiers

$Y$ is my outcome variable, $D$ is the treatment, $Z$ is the instrument, $Y_i$ is the outcome when $D=i$, $D_i$ is the treatment when $Z=i$ I don't understand the following result : when ...

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