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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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
3 votes
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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 ...
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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
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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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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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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
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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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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
51 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
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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
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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
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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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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
1 vote
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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
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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
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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 answer
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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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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
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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
1 vote
1 answer
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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
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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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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
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26 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
98 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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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
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1 answer
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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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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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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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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
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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
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3 votes
1 answer
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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
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24 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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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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1 vote
1 answer
64 views

Does estimating continuous treatment effects make sense in this case?

I was reading the tutorial for twangContinuous here. I loaded their dataset dat and found that there are two treatment groups A and B and there are different ...
SFCha's user avatar
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2 votes
1 answer
154 views

When controlling for confounders in a causal study, should we always expect a decrease in the treatment effect estimate?

In a causal study, can treatment effect go up upon applying causal techniques when comparing with the naive / simple difference in means? I think this question is method-agnostic, but for transparency ...
user11513145's user avatar
0 votes
1 answer
65 views

nonparametric test for statistical significance for control and treatment [closed]

I conducted an experiment called "die-under-the-cup" (DUTC) and now I'm looking to assess the statistical differences between a control group and a treatment group. In the treatment group, ...
Schwa97's user avatar
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1 vote
0 answers
35 views

How to generate placebo test for random false treatment?

I would like to do a placebo test to ensure the result of my main staggered DID results. I have a country-level mandate that goes into effect in different years during my sample period, and in some ...
Saleh's user avatar
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1 vote
1 answer
47 views

Can r.m One-way ANOVA be used to compare differences between groups, if those groups are made up of median values?

Essentially, I'm trying to see whether a drug affects a latency measure. I have $n=10$ mice, each with $15$ individual latency recordings (in seconds) per drug dose (repeated measures design). Most of ...
louiseinger's user avatar
2 votes
1 answer
67 views

How to check if two segments of an A/B test have significant difference in the treatment effect

I am planning to launch an A/B test and there's a concern about a possible primacy/novelty effect. I've decided to segment users into two segments (New Users, Returning Users) and estimate if the ...
Eugene Krall's user avatar
2 votes
3 answers
81 views

Baffled by Rubin's Potential Outcomes RE: What Would Have Happened?

Background Seeking a clear and authoritative explanation of a key concept of Rubin's Potential Outcomes Framework that is causing this hapless OP enormous grief. While the necessity to distinguish ...
Plane Wryter's user avatar
0 votes
1 answer
81 views

Is logistic regression with random effects appropriate for my problem?

My problem is the following: I have 1/0 "conversion" outcomes. I have samples from three groups: people who speak russian, people who speak japanese, people who speak portuguese. i expect ...
Estimate the estimators's user avatar
1 vote
1 answer
44 views

Repeated measures - sort of?

I do mostly econometrics but am faced with a repeated measures type sample. I can solve it as a regression with dummies, but feel like I'm losing information in the repeated cases. I know the medical ...
UNDMac's user avatar
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4 votes
1 answer
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In clinical trials, what is the benefit of using a composite rather than individual outcome

I have read that composite outcomes are common in cardiovascular trials because they give a holistic view of a treatment effect. Assuming outcomes are frequent enough for statistical power to not be ...
Geoff's user avatar
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1 vote
0 answers
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Multiple covariates as treatment in double ML

I have multiple economic indicators as covariates such as employment rate, gdp growth, average wage, etc. I want to estimate each of them's effect on travel demand. I was thinking of two steps: Make ...
tomtomxu's user avatar
0 votes
0 answers
19 views

Significance of pie-charts in representing sufficient-cause components

In the book Causal Inference - What If, the authors present a form of pie-chart-type diagram to represent sufficient-cause components (refer to figure 5.1 on page 64 and figure 5.2 on page 65 ...
Anirban Chakraborty's user avatar
5 votes
3 answers
700 views

Why do we talk about "everybody vs nobody" scenarios in causal inference?

Researcher A and researcher B survey mothers in Germany and ask them: $T =$ Whether they smoked during their pregnancy $Y =$ The birthweight of their baby $\boldsymbol{X} =$ All kinds of things that ...
suckrates's user avatar
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3 votes
1 answer
146 views

Why have both standardization and IP weighting, when they are fundamentally similar?

In Causal Inference: What If by Hernan and Robins, I came across two methods to find causal effect - standardization (page 19) and IP weighting (page 20). It seems both methods are fundamentally same. ...
Anirban Chakraborty's user avatar
4 votes
2 answers
548 views

Isn't strong ignorability an incorrect assumption in complex causal structures?

I have seen that in many papers/competitions for causal inference, the assumption of strong ignorability is made - $P(Y^{x}\perp X\mid V)$, where $X$ is the treatment, $Y$ the outcome and $V$ ...
Anirban Chakraborty's user avatar
1 vote
1 answer
223 views

Marginal Effect for Poisson Model

I am using package marginaleffects for calculating the AME of an exposure variable on a count dependent variable. I am hence using Poisson (and Negative Binomial as robustness). The dependent variable ...
Giant Steps's user avatar

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