Questions tagged [propensity-scores]

The probability of receiving a treatment given a set of observed covariates.

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1answer
21 views

Propensity score adjustments for nonprobability surveys

I recognize that propensity scores are often used for causal inference. Just to clarify from the outset, that's not what I'm interested in here. Instead, I'm looking at using propensity scores to ...
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8 views

Using propensity scores to calculate average treatment effects

I have obtained class probabilities for 0 or 1 classes ("control", "treatment"). can I also assume that these are also the propensity scores. If so, how do i use these probabilities or propensity ...
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1answer
21 views

Propensity scores and linearity in logistic regression

I know (and have read in other posts) that logistic regression isn't the only way to calculate propensity scores. But if you do want to use logistic regression for that, must you then check the ...
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1answer
22 views

How to account for dependencies in survival analysis of PS-matched cohorts with replacement?

I work on data from a cohort study (n = 3100). We want to compare outcomes of individuals with a special condition (n = 300) and their matched controls. I estimated propensity scores and used genetic ...
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1answer
33 views

Is it possible to use a multinomial outcome after a propensity score matching?

I would like to use a propensity score matching (PSM) to evaluate the effect of a treatment T on an outcome Y. In most of the papers that I have read, the outcome is continuous: health expenditures, ...
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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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Using propensity matched data for classification

I’m still learning about propensity score matching using r. I’m trying to understand and see if it makes sense to do the following: 1) match using watched_videos ...
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Does it make sense to propensity scores for re weighting samples in prediction tasks?

When reading the literature on propensity scores, the focus is mainly on estimating treatment effects (be it ATE, ATT, or else). But that, in linear models terms, is equivalent to asking questions ...
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1answer
15 views

How to analyze clinical outcomes after propensity matching

Suppose I have two propensity matched groups whose covariates are balanced. How do I analyze clinical outcomes between the groups. I would be interested in overall survival, one year survival, and ...
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Best way to analyze propensity matched cohort with small n

I have performed a 1:1 propensity match using R's MatchIt package giving me 50 matched pairs. My primary outcome is 1 year mortality, which occurred in 25 of the patients. I'd also like to analyze ...
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How to apply the propensity score corresponding to data from several years (panel data)?

My research topic is: The impact of a scholarship on the average salary of graduate students. I have a panel dataset for the years 2010 to 2016. My treatment group is students who received a ...
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1answer
34 views

Why match if you have the control data already?

I had a question about matching. I understand the benefits of matching prior to conducting a study due to potential increases in statistical efficiency/ adjustment for confounders. Let's say you're ...
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1answer
23 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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1answer
20 views

Difference between IPTW and MAIC?

Could someone describe the difference between IPTW and MAIC methodologies for indirect treatment comparison?
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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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1answer
35 views

Prop. score matching with subsequent time-varying exposure

I'm running basic propensity score matched cohorts and running a typical "intention-to-treat" analyses in patients with dementia, estimating mainly effects of treatment on e.g. mortality. Patients are ...
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1answer
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Causal analysis and treatment availability in observational studies

One of the assumptions of the Rubin causal model is positivity which, for an individual $i$, a set of covariates $X$ and a treatment assignment indicator $Z_i$, is often expressed as $0 < P(Z_i | X)...
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2answers
49 views

Multi-variable logistic regression for paired data in SPSS?

I was hoping for some assistance regarding performing a multi-variable logistic regression for a matched data set. I am not formally trained as a statistician (biology trained with basic stats/SPSS ...
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1answer
81 views

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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1answer
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does propensity score matching apply when the treament group was selected not naturally occuring?

I think of propensity score matching as a method for finding a comparison group for a naturally occurring group like students who attend private school. But if you have a group that was selected ...
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170 views

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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1answer
87 views

Propensity score matching in cross-sectional survey data and different size of subgroups

Let's say that I have a mutation in 1% of a population in a cross-sectional survey: ...
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1answer
23 views

Variance Estimation for Least Squares with Probability Weights

I'm running a simulation study and finding that the nominal SEs of the estimated coefficients when using weights in lm in R are an underestimate of the simulation SE. I have confirmed that $\hat{\beta}...
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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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1answer
30 views

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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1answer
35 views

Does normal linear regression in R overcome confounding?

When I run a linear regression model or logistic regression model in R like this lm(outcome ~ treatment + covariate) does the ...
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1answer
75 views

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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1answer
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Low propensity score among treated and high among the control units

Is it conceivable for the treated units to have propensity scores systematically lower than those estimated for the control units? Thanks in advance.
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1answer
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r propensity score matching (psm) for the same year [closed]

I am conducting PSM for my study. I used MatchIt package for my PSM. And the result gave me a similar set of control groups, but they control groups were different from the treatment group in terms ...
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Uplift modelling strategy and Impact Analysis

Overview: There are customers who are getting disabled and enabled everyday in the portfolio. Disablement essentially means, when a customer had paid for few days worth of subscription the end of the ...
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1answer
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Is it possible of overfit using Propensity score matching with the MatchIt R package?

I have a very large patient cohort and I am trying to define cases and controls whilst minimizing selection bias. Further down the line, I am using Cox regression to assess the efficacy of particular ...
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1answer
35 views

hypotheis testing in observational study with propensity score matching to reduce confounding

in observational studies, many people use propensity score matching to reduce confounding (measured co-voriates) between two groups (cohorts). But due to some unobserved confounding co-variates (not ...
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31 views

match on a variable that itself depends on matching

I want to determine whether Program X improved graduation rates at a higher ed institution. Students self-select into this program, so I'm using propensity score matching (with the ...
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38 views

Is there a way to evaluate the accuracy or misclassification rate of the linear model that the R package MatchIt uses to build propensity scores? [closed]

m1.out <- matchit(Treatment ~ co_variate_1 + co_variate_2 + co_variate_3, data = mydata, method = "nearest", ratio = 1) summary(m1.out) I am ...
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1answer
65 views

Does a doubly robust estimator magnify bias if *both* the outcome regression and inverse propensity score weighting are incorrect models?

The doubly robust estimator is a popular method for measuring the average treatment effect with observational data (assuming no unmeasured confounders): $$ \hat{\Delta}_{DR} = n^{-1}\sum_{i=1}^n \...
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1answer
121 views

Propensity Score Matching with Cox Regression

I am conducting a survival analysis with a Cox regression whereby the outcome variable (promotion to a senior role) is either 0 or 1. I am particulalry interested in the hazard rate (i.e., the 'hazard'...
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30 views

Propensity score matching in r using panel data [closed]

I want to conduct PSM using firm panel data in r. matchit(treat ~ leverage + cash + roa + mtb + asset, data=data) This gave me a result of only very similar one ...
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1answer
551 views

advantages and disadvantages of IPTW vs propensity score matching?

what are the advantages and disadvantages of IPTW (Inverse Probability of Treatment Weighting) comparing to PSM (propensity score matching) in dealing with confounding variables?
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2answers
55 views

Favored methods for overcoming selection bias (special attention to healthcare fields)?

I am frequently measuring the effect of behavioral health treatment interventions on outcomes of interest. However, comparing the relative efficacy of different types of treatment is tricky - more ...
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1answer
84 views

Using IPS(inverse probability weighting) with a deterministic policy as the logging policy

In a contextual bandit problem, why can't we use inverse probability weighting (inverse propensity score) with a deterministic policy as the logging policy? Could you give me a concrete example?
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0answers
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How to determine the propensity score classifier validity? [duplicate]

I've got a bit more background in machine learning than statistics. Let's say that I want to analyze causal effects based on propensity scores of the treatment and control group. I know that most ...
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1answer
463 views

Should I use a machine learning model to calculate propensity score?

In my study, running a simple linear model to calculate de propensity score for each example seemed to not be able to model my treatment choosing process correctly. My question is, does it make sense ...
2
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1answer
26 views

similar groups and need for propensity score matching?

If t-tests show that there is no significant difference between the control and treatment groups, is there a need to do a propensity score matching? Thank you, =sa
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1answer
241 views

Propensity score matching in SPSS

A practical question. When performing propensity score matching in SPSS v25, I get a separate sheet with all the cases and pairs. However, a small number of cases have propensity variable blank (10 of ...
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0answers
51 views

Causal Inference in a employee churn context (difference-in-differences / Propensity score matching)

For my master thesis I'm trying to determine the causes of an employee leaving a company. Currently I'm trying to study the effect that giving a raise has on employee leaving a company or not. So my ...
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In a regression to estimate propensity score, how can I build weights proportional to two different quantities?

I have a set of 88 people undergoing a treatment. My focus is on their contacts with a psychiatric service in the year before starting of the treatment, so I want an exact match wrt their previous ...
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Ok to use PSM to create treatment groups and then plug into CausalImpact? [closed]

Is it ok to use propensity score matching to create treatment and control groups and then plug these two time series into CausalImpact to estimate your treatment effect? I might want to do this, for ...
2
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1answer
86 views

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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1answer
64 views

large variance from inverse probability weighting (inverse propensity score)

I heard if the observed data that will be used in the inverse probability weighting method is too small, the estimator based on the weighting will have a large variance. Could you explain why that is ...
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56 views

Inverse Propensity Score in the paper “Doubly Robust Policy Evaluation and Optimization”

I am currently reading a paper whose link is http://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/paper-14.pdf. In the page 5, or 489, an estimation based on inverse propensity score ...

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