Questions tagged [propensity-scores]

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

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51
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5answers
33k views

How are propensity scores different from adding covariates in a regression, and when are they preferred to the latter?

I admit I'm relatively new to propensity scores and causal analysis. One thing that's not obvious to me as a newcomer is how the "balancing" using propensity scores is mathematically different from ...
34
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3answers
10k views

Propensity score matching after multiple imputation

I refer to this paper: Hayes JR, Groner JI. "Using multiple imputation and propensity scores to test the effect of car seats and seat belt usage on injury severity from trauma registry data." J ...
29
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2answers
4k views

Propensity score matching - What is the problem?

In estimation of treatment effects a commonly used method is matching. There are of course several techniques used for matching but one of the more popular techniques is propensity-score matching. ...
27
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5answers
7k views

From a statistical perspective, can one infer causality using propensity scores with an observational study?

Question: From the standpoint of statistician (or a practitioner), can one infer causality using propensity scores with an observational study (not an experiment)? Please, do not want to start a ...
24
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2answers
51k views

Why is Average Treatment Effect different from Average Treatment effect on the Treated?

In RCTs, randomisation balances unmeasured confounders and, I'm told, ATE and ATT would be the same. In observational studies, this is not possible and Propensity Scores are used in various ways to ...
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4answers
8k views

Why does propensity score matching work for causal inference?

Propensity score matching is used for make causal inferences in observational studies (see the Rosenbaum / Rubin paper). What's the simple intuition behind why it works? In other words, why if we ...
17
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4answers
37k views

Propensity score matching with panel data

I have a longitudinal data set of individuals and some of them were subject to a treatment and others were not. All individuals are in the sample from birth until age 18 and the treatment happens at ...
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2answers
1k views

Why is Propensity Score Matching better than just Matching?

Propensity Score Matching at a high level uses a framework of: Identify potential confounders from the co-variates i.e all factors which can potentially influence the subject being part of experiment ...
15
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1answer
6k views

The difference between average and marginal treatment effect

I have been reading some papers, and I am unclear about the specific definitions of Average Treatment Effect (ATE), and Marginal Treatment Effect (MTE). Are they the same? According to Austin... A ...
12
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1answer
5k views

Intuitive explanation for inverse probability of treatment weights (IPTWs) in propensity score weighting?

I understand the mechanics of calculating the weights using the propensity scores $p(x_i)$: \begin{align} w_{i, j={\rm treat}} &= \frac{1}{p(x_i)} \\[5pt] w_{i, j={\rm control}} &= \frac{1}...
11
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3answers
20k views

Understanding output of MatchIt in R

I have created a Matched Cohort using MatchIt package in R. I have the list of members who are in the treatment group and the control group. But I am unable to figure out which treatment subject is ...
11
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2answers
4k views

Propensity score weighting in Cox PH analysis and covariate selection

Regarding propensity score weighting (IPTW) when doing Cox proportional hazard modeling of time-to-event survival data: I have prospective registry data where we're interested in looking at treatment ...
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2answers
922 views

Is Propensity Score Matching a "MUST" for Scientific Studies?

Recently, I have been reading about Propensity Score Matching : If I have understood this correctly, Propensity Score Matching is used to construct control/treatment groups in scientific studies, in ...
9
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3answers
539 views

What are the use cases for Propensity Score Matching?

I have asked here whether, in order to establish causal relationships, the treated group and the control group must be similar on all covariates. The answer was no, if we control for the covariates in ...
9
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4answers
4k views

Adjust for everything you have in propensity score?

I have a methodological question, and therefore no sample dataset is attached. I'm planning to do a propensity score adjusted Cox regression that aims to examine whether a certain drug will reduce ...
9
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2answers
12k views

Nearest Neighbor Matching in R using matchit

I am using the matchit package to do propensity score matching on a data set. However, when doing nearest neighbor matching, if I use the caliper option, I get a different set of matched pairs every ...
9
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1answer
2k 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 ...
9
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1answer
3k views

Confidence interval for average treatment effect from propensity score weighting?

I am trying to estimate the average treatment effect from observational data using propensity score weighting (specifically IPTW). I think I am calculating the ATE correctly, but I don't know how to ...
9
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2answers
251 views

Propensity score matching vs non-parametric regression

I am trying to understand the benefit of propensity matching over non-parametric regression for causal inference from non-experimental data. As background: the way I understand it, parametric ...
9
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2answers
5k views

Propensity Score Matching with time-varying treatment

The basic propensity score matching procedure works with cross-section data (ie collected at a certain point in time). The popular psmatch2 command uses a dummy variable indicating that an ...
9
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3answers
4k views

Probability calibration from LightGBM model with class imbalance

I've made a binary classification model using LightGBM. The dataset was fairly imbalnced but I'm happy enough with the output of it but am unsure how to properly calibrate the output probabilities. ...
9
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1answer
9k views

ATT vs ATE in propensity score matching when using DiD estimates

According to Lee and Little 2017, when using propensity score (PS) methods, weighting on odds will generate the Average Treatment Effect on the Treated (ATT), while using subclassification and ...
9
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3answers
3k views

Different results after propensity score matching in R

I have conducted Prospensity Score Matching (in R using the R-package "Matchit"). I used the matching method "nearest neighbor". After matching I compared the treatment and the controlgroup in terms ...
8
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2answers
7k views

Use of propensity score in a case-control study

The theory of propensity score (PS) suggests that it should only be used for the cohort study because PS matches the "treated/exposed" to the "un-treated/un-exposed" groups. However, cases and ...
8
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1answer
410 views

In propensity score analysis, what are options to deal with very small or large propensities?

$\newcommand{\P}{\mathbb{P}}$I am concerned with observational data in which the treatment assignment can be explained exceedingly well. For example, a logistic regression of $$\P(A =1 |X) = (1+ \...
8
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0answers
548 views

Propensity Score Matching – How do the mechanics lead to a different result than unmatched?

The gist of propensity score matching, as I understand it, is as follows: You want to estimate the average treatment effect (ATE) of a treatment on some outcome. However, if you simply calculate the ...
7
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2answers
2k views

multiple imputation and propensity scores

I have a dataset with 1300 observations and 30 variables. One of the variables has 10% missing data, another has 5% and a third has 3%. Seeing Propensity score matching after multiple imputation I ...
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1answer
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Propensity Score Matching implementation after multiple imputation

There is a very good thread about Propensity Score Matching after multiple imputation with the articles referred: Propensity score matching after multiple imputation In the refered articles, they ...
7
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1answer
7k views

Difference between marginal and conditional treatment effect? Relating to regression vs. propensity score methods

Peter Austin has a nice introduction to propensity score methods (citation below), and he states that one of the differences between PS methods and plain regression is that PS methods give you a ...
7
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1answer
4k views

Propensity Score Analysis with continuous treatment

I have an observational dataset of about two dozen observed variables (continuous or discrete), plus a continuous variable of which I would like to measure the causal impact of on my dependent ...
6
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2answers
24k views

matched pairs in Python (Propensity score matching)

Is there a function in python to create a matched pairs dataset? e.g. ...
6
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3answers
652 views

Analysis strategy for rare outcome with matching

I'm working with a dataset of ~100,000 individuals where ~500 (0.5%) individuals received treatment. I have several continuous and count outcomes for all observations that I would like to compare ...
6
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1answer
263 views

Should I put outcome variable in Matchit::matchit ()

I would like to perform a logistic regression by adjusting for propensity score. My question is, do I have to include the outcome (binary in my case) in the propensity score calculation? Otherwise how ...
6
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2answers
115 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 ...
6
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1answer
157 views

How can I use Propensity Scores to adjust for survey non-response bias?

Say I estimate the probability that each member of my target population responds to a survey using propensity scores. I am having a hard time finding a clear explanation of how I can use the ...
6
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1answer
5k views

Calculating weights for inverse probability weighting for the treatment effect on the untreated/non-treated

I am trying to calculate weights for inverse probability weighting. For ATE and ATET the process is straightforward. For example in Stata: ...
6
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3answers
5k views

How to, or what is the best way, to apply propensity scores after matching?

I have developed a recent interest in propensity scores. I have been using the SPSS tool created by Dr. F. Thoemmes to calculate propensity scores using bivariate "treatment" variables (e.g., ...
6
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1answer
1k views

Procedure for testing covariate balance for generalized propensity score estimator

I'm working on a propensity score analysis where the treatment variable is continuous (a score from 0 to 100, let's say) rather than binary (treatment vs. control). I've been reading Guo & Fraser'...
6
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1answer
1k views

Post propensity score matching analysis

I want to look into the causal effects of an education program on a binary employment outcome (positive vs negative). I have two groups of students- one that took the education program (the treatment ...
6
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1answer
5k views

Paired or not paired? Comparing groups after propensity score matching

After matching on propensity score, e.g 1:1 matching, you obtain a matched subset of your data. The built-in functions in the Matching package, as a prominent example, compares groups before matching ...
5
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2answers
6k views

Comparing two or more treatments with inverse probablity of treatment weighting

I am working on a cardiovascular observational (i.e. non-randomized) study featuring three or more competing treatments. My preference would be to conduct the analysis first using 1:1 propensity ...
5
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3answers
849 views

Do propensity scores reflect the probability of treatment or outcome?

I am a non-statistician PhD student working on a project that has involved some propensity score matching (PSM). I had initially assumed that propensity scores would represent the probability of each ...
5
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1answer
720 views

Do I need to adjust OLS standard errors after matching?

Suppose I use propensity score matching to create a dataset of treatment and control observations. Then I run OLS regression with some covariates that were not necessarily included in the propensity ...
5
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3answers
9k views

Propensity Score Matching for more than 2 groups

I'm new to propensity score matching (PSM). So, my questions can be bit trivial. 1) Suppose I've 3 treatment levels and want to check the effectiveness of the treatment levels. Treatment levels are ...
5
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2answers
215 views

Comparing the effect of a treatment that was optional for its receivers

In 2012 we collected data at our university about retention of students from first semester to second semester along with some other variables. The retention variable is binary, 'retained' or 'did not ...
5
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1answer
2k views

Difference between using a propensity score for matching vs. regression analysis

So I am confused on what the difference is if I match patients based on propensity scores vs. using the propensity score and then applying that into a multivariate regression analysis? Is there a ...
5
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2answers
2k views

How does Inverse weighted propensity score regression differ from propensity score matching?

I understand that in inverse weighted propensity score regression, a set of weights are used to create scoring. In propensity score matching, a propensity score is created for each strata and people ...
5
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1answer
2k views

Manipulating data for propensity score matching following multiple imputation with mice package

I've completed multiple imputation of my dataset for the first time using the mice package in R. I'm familiar with the procedure for using the ...
5
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1answer
374 views

Propensity score matching linearity assumption

One of the advantage that i have herd talked about propensity score matching, vs. a regression, is that propensity-score matching doesn't rely on linearity assumptions. This seems incorrect on the ...
5
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1answer
99 views

Including already balanced confounders in propensity score model

I have a dataset that I want to run propensity score analysis on. Using package TWANG in R, I plan to compute the propensity score and use it as IPTW. The variables that I put into the model are those ...

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