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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Modelling impact of study materials towards students giving correct answers
I am new to Bayesian statistics. I have a basic idea about priors, posteriors and likelihood. I have a problem to model using bayesian statistics, and apply using python. ( this is a side project i do ...
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Is this a breach of the consistency principle in causal inference?
My understanding of the consistency principle is that the observed outcome is equal to the potential outcome. i.e. let T = treatment, if T=1 then then the Observed outcome (Y) is equal to the ...
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Bias or average treatment effect? (in a model where coefficients vary as a function of explanatory variables)
I have the following model with exogenous continuous variable $x_i$, endogenous continuous variable $y_i$, and some $k$ constant between the maximum and minimum values of $x_i$:
$$y_i=\gamma_0+\...
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Can I still call this as a difference-in-difference analysis?
I have a regression which has treatment and control groups.
I want to test how the treatment group (binary variable) reacts during crises periods compared to control group.
The crisis variable is a ...
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Effect of treatment on outcome variance
I am in the context of an observational study, but let's take as an example a randomized control trial studying the effect of treatment $T$ on outcome $Y$.
A difference-in-means test indicated no ...
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Comparing treatment effects across groups
I created two donation campaigns, one loss framed (N:1993) and one gained framed (N:1989) I understood donations are usually veeeery low and the sample was a lot smaller than previously expected, so ...
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Are the Grenander conditions on the explanatory variable to ensure OLS has consistent treatment effect estimates applicable to GLM/GLMM?
Are the Grenander conditions on the explanatory variable to ensure OLS has consistent treatment effect estimates applicable to GLM/GLMM where you have count,binary data?
If you don't know what ...
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Difference in difference regression after propensity score matching
I’m doing some matching followed by difference-in-difference regression to look at the impact of a certain disease on people's income. The matching is done, and I’m preparing to go into the next stage,...
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Conditioning on a future (post intervention/treatment and post outcome) event in a causal diagram?
My team is conducting a pre/post-intervention comparison of health outcomes in treatment and control groups, and the question came up whether it's a good idea condition/match on a deceased flag for ...
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Can I Calculate a Local Average Treatment Effect (LATE) With Three Treatment Groups?
This is a conceptual question About Local Average Treatment Effects (LATE).
Basically, I ran an experiment that included three treatment groups and a control group. We measured our outcome variable at ...
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Multiple treatment dynamic diff-in-diff
I have a question regarding the question here. In @Thomas Bilach answer, he gives the following example, which I have edited a bit more to add my own hypothetical examples:
$$
\begin{array}{ccc}
item ...
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How do adjust for the confounder of a confounder? we call the confounder of a confounder for treatment effect estimation?
How do we adjust for the confounder of a confounder in order to compute unbiased estimates of the treatment effect of $A$ on $D$? See the causal graph (DAG) below:
What do we call the confounder $C$ (...
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Meta-analysis effect size computation
I've been working on a meta-analysis off and on for a while. I am at the analysis stage. Meaning that all relevant effect sizes have been computed. However, I need help understanding what to do when a ...
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Compare two groups with differnt treatments and different populations
I will simplify my research question. I have 40 patients (group 1) with assessed a variable v1 (e.g. their blood preassure) at timepoint 0 ( v1(t0) ). After treatment (treatment 1 at timepoint 1) I ...
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How do I estimate the Average Treatment Effect on the Treated in a Difference-In-Difference Model?
Suppose the DID model is
Y = a + b(Treatment) +c(Time) + d(Treatment*Time). Now, If i wanted to find out the average treatment effect on the treated, how should i modify my model?
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BACI analysis where impact variable is continuous
I am analysing data from a before-after control-impact (BACI) experiment where all 10 sites (replicates) were measured once before a disturbance, then 5 sites were subjected to the disturbance, and ...
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Relationship between blocks, factors and treatments
I have recently began studying a course on Designed Experiments and am having some trouble understanding some of the terminology. I've looked at some other answers on the site and I think that I am ...
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How to compare plant response to stress?
I am looking for advice on analysis of a greenhouse experiment.
I had 3 levels of stress (drought) treatments, and seeds were from 4 enviromental "Sources". I grew 5 plants per "Line&...
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Does Random Sampling Affect Significance of Regression Result?
When a research design includes treatment and control group and the number of samples in the treatment group far exceeds that in the control group due to the nature of a data set, the treatment effect ...
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Relevance of baseline covariates in the reference group when estimating ATT
Let's say we have a single treated group A, and then two reference groups B and C. The two ...
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Effect sizes for linear mixed effects model
I wondered how I could estimate effect sizes for individual coefficients in my mixed effects model.
It consists of two categorical predictors, defining group and time of measurement (main effects and ...
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Is the ATE or ATT generated from Iverse Probability Weighting?
What effect is estimated from the execution of IPW?
When running IPW in R, I know that you specify the formula for the weight itself, i.e.:
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Average Treatment Effect after Propensity Score Matching
I used the "Matchit" package in R to perform propensity score matching on my data set and want to calculate the average treatment effect afterward. I use a caliper of 0.2 Std. deviations and ...
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Correct statistical test to answer: "did my intervention help my patients?"
I am doing something to a set of $N$ patients (keeping this vague for generality).
In order to assess if my intervention/treatment is making any difference I did the following:
Before my intervention ...
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Which test for 2x2 treatment design with continuous dependent variable?
We conduct a behavioural experiment with 2 treatment dimensions, let's call them A and B. A and B each have 2 manifestations so there are A0 A1 B0 and B1. Participants of the study are randomized into ...
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Estimating the impact of a pricing policy
I am trying to measure the impact of a pricing policy using panel data from a popular online marketplace. Specifically, I am applying a Diff-in-Diff model in which some products (treated sample) ...
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Interpretation PCA Index
I have a data set with four outcome variables, two regarding law and order (LAO) and two regarding pain (POI). LAO variables are survey measures on likert scales ranging from 0 to 6 and from -5 to 5. ...
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IV weights for binary instrument and discrete treatment
Suppose we have we the structural equation where Y is continuous and T discrete. An example could be $Y$ is wages and $T$ is years of schooling
$$ Y = \alpha + \beta T + u$$
We have a binary excluded ...
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Difference-in-differences parallel trends for treatment and control groups of uneven size
I am confused about how to run a formal parallel trends test for my DiD study. Here is an overview of my data.
There are 25 districts in the treatment group. Each district has data on district-level ...
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Experiment Design with Known, Targeted Probability of Receiving Treatment
Is there a name in the literature for the following experimental design?
Suppose we have $i=1,...,n$ individuals in our sample. Denote their baseline covariates, binary treatment assignment indicator, ...
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Distinguishing between size of compliers and never-takers in control group
Say we have data on the total number of units assigned to the control group (1200) and their outcome (20%). We are dealing with one-sided non-compliance, and have data on number of never-takers in the ...
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How to setup a GLM in R for treatment effects with multiple different controls
Setup: 6 different types of controls, 1 treatment
Measure: Counts (between 0 and 100)
Data: Each control and treatment group only has one measurement
Variable=Value
C1=1.9
C2=2
C3=2.1
C4=3
C5=2.5
C6=2....
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Difference in Difference Model for Tariffs
I am looking at exports of products that have been affected by tariffs with the control group as none tariff targeted products and the treatment group as tariff targeted products. How do I specify a ...
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Control function interact treatment with residual
I am going through an old metrics tutorial using instrumental variables regression. In one question we are supposed to estimate the Average Treatment Effect (ATE) using a control function approach. We ...
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Treatment effect measured by Likert scale
I'm analysing data from a study with a control and a treatment group.
There are four questions on purchasing decisions for different products. Each question uses a 4-point Likert scale asking ...
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Translating marginal effects for layperson
In a setting with a binary outcome logistic model focusing on a binary treatment, how would you translate the average marginal effects to make it understandable to a layperson?
Let’s suppose the ...
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Interpreting IV for continuous treatment and continuous instrument
Most standard IV framework looks at cases when both treatment (let's denote by $D$) and instrument (let's denote by $Z$) is binary. Let $Y(D)$ be the potential outcome (by using this notation, I'm ...
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Interpreting Average Marginal Effect in raw numbers
I fitted a logistic regression model with a binary exposure variable X, trying to understand the effect it has on a given outcome Y, which measures infection (yes/no). I calculated the odds ratio of ...
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Differece in Difference (pooled cross section) for donation data
I am trying to estimate the effect of a policy that "should" have affected charitable behaviour within the US. To test this, I have data on daily donations for an NGO from all donors (global)...
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What is the 2SLS bias with a zero first stage in instrument analysis
I am trying to understand the impact in the IV estimate when zero coefficient is obtained in the first stage.
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correcting control-treatment group sizes as expected events (in control) differed
I did a study were my control group (n=6) had an event in 100% of the individuals.
I compared the time-to-event of the control to treatment groups (3), each with 6 individuals. The effect size is ...
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Difference of baseline data between groups NOT significant on pre-matching [duplicate]
In a retrospective observational study, I'd like to compare the efficacy of drug A vs drug B, and was considering propensity score matching on age, gender, history of diabetes mellitus, and history of ...
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Estimation of two successive treatments - Differences in Differences
I am trying to estimate the effect of two successives policies on individual subjects.
I have a panel data following individuals each 3 years from 2005 : 2005, 2008, 2011, 2014 and 2017.
The first ...
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How to estimate which user features predicts the treatment effect of an AB test
I have an AB test with a target variable (Y), M user features and N other features. The user ...
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Panel data regression with time varying treatments and fixed effects
Experts,
I have some trouble concerning my regression model for a panel data analysis. The dataset includes observations of 200 firms over a period of 6 years (2000 - 2005) regarding merger activities ...
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How to test for pre-trends in a DiD setting with repeated cross sectional data?
I have a dataset consisting of repated cross sections spaced out every two years (e.g, 2008, 2010, 2012...). I am running a DiD specification to identify the causal effect of treated individuals after ...
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Is "triple differences" and estimating "heterogeneous treatment effects" the same?
I am running a difference-in-differences (DiD) regression with a time and treatment interaction and I have a continuous outcome variable. However, I hypothesize that the DiD estimator can be moderated ...
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How can i calculate the relative ATT (as percentage) using propensity score matching method?
I am using a propensity stratification method (in dowhy package). below is a table of my data. It splits at each strat the number of users in drug population vs not. Note that the strata are split ...
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Positivity assumption in causal inference with continuous covariates
In causal inference, studies usually require several assumptions (e.g., Unconfoundedness) to make valid causal statements. One of these assumptions is the 'Positivity' Assumption (sometimes referred ...
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In DoubleML how is model hyperparameter chosen?
I'm referring to DoubleML pkg's example in python here or R here, for example cell 27 where 'set_ml_nuisance_params' was used to set model hyperparam. For those familiar with the package, how are ...
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