Questions tagged [propensity-scores]

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

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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. ...
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18 votes
4 answers
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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 ...
Andy's user avatar
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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 ...
lsfischer's user avatar
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6 answers
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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 ...
Frank Barry's user avatar
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Difference between covariates and treatment confounders in propensity score matching

Here, I have the definition of a propensity score: Propensity score is defined as the conditional probability of assignment to a treatment given a vector of covariates including the values of all ...
4956's user avatar
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Matching with Multiple Treatments

What's the best way to use matching methods with multiple treatment groups? I'm assessing the impact of an intervention on an outcome. For my first analysis, I used the MatchIt package (see code below)...
rubyrose's user avatar
2 votes
2 answers
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Survival Analysis, Cox Regression in randomized trial vs. observational study and propensity score matching

In randomized clinical trials in the efficacy part, often survival analysis is used to analyze the time-to-event data. Since it is randomized (if randomization was done properly) one can assume that ...
Stat Tistician's user avatar
35 votes
3 answers
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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 ...
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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}...
RobertF's user avatar
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11 votes
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732 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 ...
Shade's user avatar
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1 answer
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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 ...
Anthony Nash's user avatar
5 votes
2 answers
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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 ...
Giuseppe Biondi-Zoccai's user avatar
4 votes
3 answers
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What is the equation for random forest?

I need an equation for random forest so that I can score fresh data I receive every week, based on beta estimates I got after building model using this ensemble methodology. Every week I do not want ...
user3459010's user avatar
3 votes
1 answer
761 views

How does propensity score matching that uses only a small proportion of eligible patients affect generalizability?

I am reviewing a paper that seeks to assess the effect of treatment on mortality using observational data about 2,985 hospitalized patients. A propensity-matched analysis ends up with 380 patients (...
Diana Petitti's user avatar
2 votes
1 answer
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Matching is not recovering the true effect in simulated data

I am trying to recover the true (simulated) effect of a treatment Z on an outcome Y, which is set to ATE = 5 (the csv file for the data is located here: https://www.dropbox.com/s/92obn9hsu3tqy92/...
Adel's user avatar
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34 votes
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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 ...
bobmcpop's user avatar
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17 votes
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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 ...
RayVelcoro's user avatar
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9 votes
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Inverse probability weighting (IPW): standard errors after weighting observations

When using propensity scores for inverse probability weighting (IPW) the standard errors for the parameters in the regression model may be affected. I have seen several examples of people using ...
Rob Hall's user avatar
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8 votes
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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 ...
Misha's user avatar
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7 votes
3 answers
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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., ...
Behacad's user avatar
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Propensity Scores Weighted DID

I have a tough challenge using the DID. I have only 2 year data set, 2010 and 2015. The first is the baseline and the latter is follow-up year. In order to obtain true causal effect using DID, I ...
putut purwandono's user avatar
5 votes
1 answer
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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 ...
MikeS's user avatar
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4 votes
2 answers
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Generating a propensity score for multiple treatment using multinomial logistic regression

I am analyzing data from a representative cohort (>10,000 persons, 10 years follow-up) and I would like to perform a retrospective cohort study comparing the effect of a treatment on the outcomes. ...
Vincent's user avatar
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1 answer
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How to do PSM with panel data using PanelMatch?

I would greatly appreciate if you could let me know how to use PanelMatch for my dataset. Unfortunately, I couldn't find it's manual so I don't know how to find which firms are matched, how to extract ...
ebrahimi's user avatar
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27 votes
5 answers
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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 ...
19 votes
4 answers
10k 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 ...
max's user avatar
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13 votes
2 answers
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What are the pros and cons of using mahalanobis distance instead of propensity scores in matching

I learned about this option of using mahalanobis distance instead of PS to do matching from the matchit() function in R. It seems a more nonparametric approach. Could you state its pros and cons and ...
hehe's user avatar
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10 votes
4 answers
5k 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 ...
Adam Robinsson's user avatar
6 votes
2 answers
203 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 ...
ShannonC's user avatar
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5 votes
1 answer
444 views

Why does propensity score matching fail to estimate the true causal effect when OLS works?

Consider the following model (DAG), where D is the treatment (exposure) and Y1 is the outcome. To estimate the causal effect of <...
giac's user avatar
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1 answer
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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 ...
Aneesh's user avatar
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3 answers
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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 ...
Beta's user avatar
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2 votes
2 answers
821 views

Propensity Score

What are the various methods used for binary classification other than logistic regression? What are the advantages of logistic reg. model in developing Propensity score w.r.t. other methods? ...
user2035158's user avatar
2 votes
1 answer
1k views

Propensity score match with exact matching on one variable

I would like to compare survival outcomes of two groups (control vs. treatment). Because of imbalances of baseline covariates, I used propensity score matching using nearest neighbor matching. After ...
Takanashi's user avatar
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1 vote
1 answer
69 views

Is a propensity-score match necessary if my pre-match covariates are already balanced?

I planned to run an analysis starting with a dataset of 22,000 records. From this set, I would conduct a match to obtain two balanced groups then look at my outcomes of interest. I was planning to use ...
Dylan Russell's user avatar
1 vote
1 answer
2k views

how to calculate manually propensity score weights for multinomial treatments where one of them is baseline

I want to get intuition into the calculation of propensity scores (PS) and inverse probability of treatment weights (IPTW) for a multinomial treatment using multinomial regression. One of the ...
Krantz's user avatar
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11 votes
3 answers
10k views

Probability calibration from LightGBM model with class imbalance

I've made a binary classification model using LightGBM. The dataset was fairly imbalanced but I'm happy enough with the output of it but am unsure how to properly calibrate the output probabilities. ...
Auren Ferguson's user avatar
11 votes
2 answers
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 ...
Kjetil Loland's user avatar
11 votes
3 answers
4k 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 ...
Breeze's user avatar
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11 votes
1 answer
11k 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 ...
JJM's user avatar
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10 votes
0 answers
605 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 ...
Yakkanomica's user avatar
10 votes
2 answers
13k 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 ...
Sam's user avatar
  • 203
10 votes
2 answers
6k 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 ...
Jhonny's user avatar
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8 votes
1 answer
2k 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 ...
badmax's user avatar
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7 votes
1 answer
984 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 ...
Alex's user avatar
  • 497
6 votes
2 answers
4k 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 ...
user321627's user avatar
  • 4,524
6 votes
3 answers
800 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 ...
radek's user avatar
  • 1,397
6 votes
2 answers
902 views

Proving equation 3.3.12 in Angrist Pischke Mostly Harmless Econometrics (inverse probability weighting formula for ATT effect)

On page 82 in Mostly Harmless Econometrics, there is a formula for the average treatment effect on the treatment, using propensity score, that is $$\tag{1} E(Y_{1i} - Y_{0i}\vert D_i =1) = E\bigg[\...
user106860's user avatar
6 votes
1 answer
1k 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 ...
StatisticalPig's user avatar
6 votes
0 answers
7k views

Propensity Score Matching in R with Multiple Treatments

I commonly estimate Average effect of Treatment on the Treated (ATT) via Propensity Score Matching for a particular business use case. I'm currently using R, where ...
Hack-R's user avatar
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