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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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Diff-in-Diff with multiple time periods

this is my first post here and I'm very new, I hope this is somehow clear. For context: For my master thesis, I want to estimate the effect that replications have on the citations of a paper. For this,...
LauraGonzalezGa's user avatar
1 vote
2 answers
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Looking for a statistical test for significance of association between a treatment and non-mutually exclusive categories

I am looking for a statistical test which will give a p value for the association between a treatment and two potentially co-occurring labels: I have two categories of cells: state A and state B. A ...
Richard's user avatar
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2 votes
1 answer
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Standard error clustering with few clusters

I am trying to estimate the causal effect of police presence on crime rates using county-level crime data from 166 counties across three states. The treatment is assigned at the state level. For my ...
Schwa97's user avatar
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11 votes
3 answers
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How can restricted randomization to achieve covariate balance lead to imbalance in unobserved variables?

In literature, designing an experiment is considered a trade-off between covariate balance and robustness. For example Harshaw et al. (2024) writes In an effort to make the estimators more precise, ...
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How to compare real-time physiological data between pre-defined time blocks

I have a set of physiological data (skin conductance, skin temperature, heart rate) for approx. 20 patients measured per second over approx. 8 to 10 minutes during a virtual reality exercise. The data ...
Richard Tinkler's user avatar
3 votes
1 answer
65 views

Diff-in-diff with an unbalanced panel

Say I want to study the effect of some intervention using a diff-in-diff setup. I have a panel of units observed during some period. I can identify treated/non-treated groups and pre/post periods (...
chris_chris's user avatar
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What is the connection of a partial regression plot and a partial dependence plot?

We have a partial regression plot as defined here: https://en.m.wikipedia.org/wiki/Partial_regression_plot It's usefulness is discussed in What does an Added Variable Plot (Partial Regression Plot) ...
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My mixed poisson regression shows cross-effects between blocks. How do I report this?

I am analyzing salivary biomarker concentrations from pigs in three blocks. The saliva was taken at three sampling points(variable Time), and I have four treatments. My fixed/main effects are ...
Siba Khalife's user avatar
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Techniques for uplift modelling/Conditional Average Treatment Effect(CATE) estimation for observational data

I have very recently started learning CI and was going through this very famous paper:https://proceedings.mlr.press/v67/gutierrez17a.html which mentions that Randomised Control Trials are an essential ...
Abhay Gupta's user avatar
2 votes
0 answers
24 views

Re-calculate accuracy, precision and recall after treatment effect in a model

Working in a churn-prediction model where the goal is to detect the players that have a high chance to churn from the site and send those players an offer to keep them in site. In the initial training ...
ELTono's user avatar
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2 votes
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What estimator and R package can be used for staggered difference-in-difference with (non-panel) cross-sectional data, controls and interactions

I am trying to run a difference-in-difference analysis in R. My data is non-panel, so I am reliant on a TWFE model where I have groups of individuals who are ...
flâneur's user avatar
4 votes
1 answer
114 views

Inferences on ratio of branch means in randomized experiment

It's generally well known that the difference of means is an unbiased estimator of the Average Treatment Effect in randomized experiments: $\mathbb{E}[Y|A=1]-\mathbb{E}[Y|A=0]$ is unbiased for $\...
user1993951's user avatar
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22 views

How can I convert effect estimates to be on the same scale? [closed]

I am doing a meta-analysis of studies that examine the effect of air pollution (continuous exposure) on a health outcome. Many of the studies report effect estimates for different units of exposure. ...
embracent's user avatar
1 vote
0 answers
105 views

Assessing whether the probability of being assigned treatment is equal (or reasonably close) between two individuals/groups [closed]

I'm currently studying the textbook Design of Observational Studies, second edition, by Rosenbaum. Chapter 3 Two Simple Models for Observational Studies says the following: 3.1 The Population Before ...
The Pointer's user avatar
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Compairing two groups with paired data

I have two groups of samples: healthy and diseased groups. Each had an X treatment. So, I have a healthy baseline and healthy X treatment. The same for the disease group. Which is the best way to ...
Biologist09's user avatar
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Estimating treatment effect with/without intercept [duplicate]

I am trying to estimate the treatment effect based on the above two model: $$Y(Z)=\beta_0+\tau Z+\varepsilon.$$ $$Y(Z)=\tau Z+\varepsilon.$$ Based on result from my data, I found the intercept is not ...
Fangzhi Luo's user avatar
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OLS estimator and conditional variance weighting

I'm reading Counterfactuals and Causal Inference by Morgan and Winship. In chapter 6, they discuss OLS as a means of estimating the average treatment effect for a binary exposure $D$ (assuming all ...
Demetri Pananos's user avatar
4 votes
2 answers
110 views

Should this be a two or three factor ANOVA?

I can't work out if I should be using 2 or 3 factors here. I have two groups: CON and TEST I gave Drug A or Drug B (there is no "None" group...) I take samples before treatment and after ...
DS14's user avatar
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1 vote
1 answer
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Average treatment effect (ATE) estimation via matching method while outcomes of control population are constant

I want to estimate the average effect of a treatment that was given with a selection bias. To do this, I'd like to use a matching method. Basically, this method involves finding, for each treated ...
HnbBarca's user avatar
2 votes
0 answers
52 views

IPW Weights for Marginal Structural Models for Different Estimands

As Blackwell and Glynn 2018 note, an interesting property of marginal structural models is their ability to estimate treatment effects that account for the dynamic properties of panel data. For ...
Brian Lookabaugh's user avatar
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What happens to the parallel trends assumption when we estimate unit fixed effects at the micro level in TWFE?

I am a bit confused about the recent difference-in-differences/ two-way fixed effects literature. For example, in this paper the authors analyse a policy effect that happened at a state level on ...
Izzy's user avatar
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1 vote
1 answer
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Analyzing Intervention Effects in a Natural Experiment with Uneven Measurement Points

I am currently working on an observational study aimed at understanding the impact of a certain intervention within a natural setting. Our dataset includes two distinct groups: a treatment group that ...
yelena's user avatar
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0 answers
22 views

Using weights in prognostic models of Survival Analysis data

I have a dataset where I'm comparing Survival (overall and cancer-specific survival) between two treatment groups (Surgery vs. Radiation) for prostate cancer. As suggested by Noordzij et al (PMID ...
Uro_Star's user avatar
4 votes
1 answer
149 views

how to deal with treatment dropouts in experimental designs

I conducted a between-subjects experiment with one 3-level factor (high group vs low group vs control group). Because of dropouts from the treatments, the final distribution is control group: 85 ...
dondu's user avatar
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5 votes
1 answer
102 views

Performing a Difference-in-Difference Analysis where the Control Group is Already Treated

I'm involved in a project where the outcome is the proportion of cancer patients who have received surgery. The treatment event is a state-level policy change that mandated moving all these cancer ...
user8408275's user avatar
1 vote
1 answer
95 views

Difference-in-differences when treatment status is revoked

I am trying to analyze the effect of receiving "elite" status on a university's number of international first-year students. I have a perfectly balanced panel of 17 universities over an 18-...
Stephanie's user avatar
1 vote
0 answers
26 views

A complex crossover study: addressing unbalance and time effects

I'm analyzing a crossover study, where subjects are measured at two time points during each treatment phase (Placebo or Treatment). Additionally, baseline measurements were taken before the experiment ...
Diego Pujoni's user avatar
2 votes
1 answer
93 views

Is it possible to use simulated (bootstrap) A/A tests of historic data to estimate the impact of confounding factors on the treatment effect?

I recently heard a proposal for a method to measure the degree to which confounding variables impact historic results of A/B tests. In order to ascertain the degree to which confounding has impacted ...
McGez's user avatar
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4 votes
1 answer
175 views

Event study renders treatment effect significant

Currently, I am working with crime panel data (county-day level over several years) to investigate the effect of police presence on crime. I use an "exogenous" increase in police presence ...
Schwa97's user avatar
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0 answers
19 views

Marginal Treatment Effects using MTEFE - postestimation discrepancy

I am currently calculating Marginal Treatment Effects for an outcome Y (earnings) and a treatment D (joining sector 1 vs sector 0), using the MTEFE package from Stata. I use a separate approach with a ...
Clara HL's user avatar
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0 answers
33 views

estimating effect with marginaleffect package

I want to estimate ATE. first of all I used MatchIt package for full matching for propensity score and then I used logistic regression with all of variable in propensity score model after that I used ...
Mahboobeh Taherizadeh's user avatar
1 vote
0 answers
35 views

How to estimate interpretable treatment effects using a marginal structural model?

Say that I estimate a marginal structural model with weights obtained by inverse probability weighting. Imagine that my model looks something like: $Y_t = X_t + X_{t-1}$ again, with observations ...
Brian Lookabaugh's user avatar
0 votes
0 answers
23 views

When to calculate Outcome value in Propensity Score Matching with staggered adoption?

Sorry if this is a basic question (I'm no statistician): In a propensity score matching study, WHEN do you calculate the outcome values (for each treated and control unit) needed to compute the ...
Romain's user avatar
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1 vote
1 answer
26 views

Using an Event Change as Treatment in a Regression

This is probably a fairly simple question, but it is one that I have not considered until now. Imagine that I have some $Y$ of interest (let's say, body fat percentage), and I also have some $X$ of ...
Brian Lookabaugh's user avatar
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0 answers
35 views

Caclulating the standard error of difference in Conditional Average Treatment Effects (CATEs)

Some perceive scientific information (for example, scientific evidence of climate change) as accurate, but others don't. I want to know under which condition this biased evaluation increases or ...
Jin's user avatar
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2 votes
1 answer
61 views

Simulating Estimates with Potential Heterogenous Treatment Effects

To verify whether a given model can accurately estimate the target estimand of interest, one might generate data to simulate the assumed data generating process, define a treatment effect, and ...
Brian Lookabaugh's user avatar
2 votes
1 answer
36 views

Can I use kmeans on paired data?

I want to see if a treatment brings patients closer to controls using multiple dependent variables. Can I do kmeans and see if the controls are separate from the patients before treatment, but cluster ...
maglorismyspiritanimal's user avatar
1 vote
0 answers
20 views

Is There a Standard Metric for Evaluating Treatment Impact Considering Action Cost in Uplift Models?

I'm currently exploring Uplift modeling, specifically the use of the Conditional Average Treatment Effect (CATE) metric: $$ \tau(t', t, x) := \mathbb{E}[Y | X=x, T=t'] - \mathbb{E}[Y | X=x, T=t] $$ ...
Amit S's user avatar
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0 votes
0 answers
9 views

Static risk prediction models and years-to-live stratification

A known paradox is the fact that although the absolute risk for developing a disease may rise with age, the conditional probability for every person to develop a disease, given that he hasn't ...
zvika segal's user avatar
0 votes
0 answers
30 views

How to find ATE in panel data when only post treatment values are available?

I am working on a problem for understanding the impact of a customer acquisition strategy by a company. Here, the company has run a few strategies for acquiring customers, which are online ads, ...
skdhfgeq2134's user avatar
5 votes
1 answer
101 views

Modelling treatment strength in a mixed-effects model?

I'm analyzing an experiment where the strength of the treatment varied by treatment day in an uncontrolled way. Specifically, we tested the response time of 11 non-overlapping groups honey bees on 4+ ...
Tim's user avatar
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2 votes
0 answers
24 views

Overfitting if estimating multiple target parameters in dataset (avoiding Table 2 Fallacy)?

For my job I'm sometimes asked to find the driving factors behind a healthcare outcome such as total annual cost or # of inpatient visits. "Driving factors" typically means reporting average ...
RobertF's user avatar
  • 6,194
1 vote
1 answer
77 views

Do we need to generate new IPW when doing subgroup analysis?

I am working on a quasi-experimental study to compare an interventional and control arm. I have generated inverse-probability weights (IPW) and weighted the population (N=300) when estimating effect ...
tatami's user avatar
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0 answers
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Control Variables in RD design

There is a large concern about using control variables that are not predetermined (i.e. may be affected by a treatment), in DID and Matching approaches. How is this in an RD approach after the newest ...
cascom's user avatar
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0 votes
0 answers
47 views

Derivation of the formula for E(MSE) in Recursive Partitioning for Heterogeneous Causal Effects (Athey and Imben 2015)

This post answers a question from the Recursive Partitioning for Heterogeneous Causal Effects paper by Athey and Imbens. However, I am blocked at an even earlier stage from the previous post. I don't ...
timothee_stat's user avatar
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0 answers
31 views

What is the correct specification of covariates/matching variables for exact matching when estimating effects with MatchIt in R?

First, I understand that including covariates in the outcome model after matching is optional based on reading the matchit vignette and similar question here. However, I'm a bit confused on what is ...
llamatrauma's user avatar
0 votes
0 answers
35 views

Correlation between two groups over a set of treatments for a continuous value

I have two groups tested with a set of treatments, and the measured quantity is continuous. Is there a good way to compute the correlation between the two groups? For example, I have two types of ...
subhacom's user avatar
  • 101
3 votes
1 answer
91 views

Measuring the effect of treatment on variable over time

I am trying to establish whether a treatment had a significant effect on a variable over time. I have raspberry plants treated with $5$ concentrations of compost ($0$%, $5$%, $10$%, $15$% and $20$%) ...
B.Shermeister's user avatar
0 votes
0 answers
28 views

Effect modification on the absolute scale

Usually when talking about effect (measure) modification or treatment effect heterogeneity, we are talking about heterogeneity in the relative effect under two interventions in some subset of the ...
elbord77's user avatar
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0 votes
0 answers
34 views

Stepped wedge design to analyze healthcare intervention rolled out at different times to members?

I'm working on a pre/post analysis of longitudinal healthcare data where an intervention program was implemented on a member by member basis over a period of several years. Some eligible members ...
RobertF's user avatar
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