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Questions tagged [matching]

Matching refers to a process in experimental design in which observations are sampled in a systematic, non-random fashion to be analyzed more efficiently with special statistical methods.

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What are the possible solutions for performing full matching on a dataset with a size of 61,000? [closed]

I am working with a dataset of approximately 61,000 observations (with 27,686 treatment and 32,405 controls) and need to perform full matching using the MatchIt ...
eshuns's user avatar
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Use SMD or raw difference in proportions when comparing balance of binary covariates used in propensity matching?

I am a junior in the medical field, and have been tasked with performing a propensity matched analysis of some data, using R. I am looking at comparing the balance of my covariates (both before and ...
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Matching on actual earnings versus matching on the kind of unexpectedness in earnings

For simplicity, lets assume this is a question about linear modeling, although I am actually looking at some non-linear models and am willing to consider other models if they would be more ...
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How to compare cases and controls after adjusting for age differences?

I have a dataset in which I have different measures (a, b, c) and two independent variables: D which is an independent binary variable representing cases (1) and controls (0), and age is another ...
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General question about exact matching on variable/s

I haven't used matching a lot but have certainly read many papers (some quite old and obviously before propensity scores were used much) that have talked about 1:1 matching on age, sex, etc variables ...
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How to deal with exposure status change and outcome contribution in different exposure status?

I want to estimate the risk of cardiovascular events in patients with diabetes (exposed group) compared to patients without diabetes (unexposed group). How do we deal (in the analysis) with patients ...
Nao's user avatar
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In Matching, are capped confounders a legitimate method of improving balance for matching with highly skewed continuous confounders?

In matching (or similar confounder-control methods such as weighting), are "capped metrics" a "legitimate" method of improving balance for highly-skewed continuous confounders? ...
user11513145's user avatar
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1 answer
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After matching: How do I interpret the value of the type ‘distance’ (=Propensity score) in the balance measures table of the r-package cobalt bal.tab?

I have used the R-package ‘MatchIt’ to perform (1) a nearest neighbour propensity score matching (NNM) based on the Framingham Heart Study and (2) for comparison, an optimal PS matching (OM) for the ...
user19939387's user avatar
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When does Matching result in an ATE vs. ATT on observational causal studies?

I have read that matching nearly always yields the $ATT$ effect, but that subclassification matching can yield the $ATE$. I am therefore wondering what is a heuristic for determining what kinds of ...
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In propensity score matching, is the estimand to be estimated the ATE or the ATT?

In the propensity score matching literature (Central Role of the Propensity Score by Rubin), the treatment effect estimand is referred to as the "Average Treatment Effect" (ATE). However, in ...
user321627's user avatar
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Propensity score matching with near-perfect differentiation

I am working on an analytics task for my job to match control units to treated units to monitor the effectiveness of a new marketing initiative. We decided to use a propensity score matching method ...
Emma's user avatar
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How to compare initial means between weighted cases?

I want to check if the mean value of a certain personality trait differs between young adults in organization X and young adults outside of the organization, who are identical in terms of education ...
MHx01's user avatar
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Is including weights in g-computation not the same as a plug-in doubly robust estimator?

In the R package vignette for WeightIt(), in the section "Modeling the Outcome", it explains that (assuming I'm reading correctly) that the purpose of applying g-computation after creating ...
user11513145's user avatar
2 votes
1 answer
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What are downsides to "genetic matching," particularly outside of causal inference settings?

Multivariate matching methods typically involve two steps. First the user computes $D$, a matrix of the multivariate distances between units. Second, the user applies a matching function (e.g., 1:1 ...
socialscientist's user avatar
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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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What are the main pros and cons of the most commonly used weighting methods?

There are many methods to generate balancing weights in observational studies (see, for example, the many methods implemented in the amazing WeighIt package). I have seen some great discussions about ...
Charly Marie's user avatar
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When to use paired samples tests? Specific case in clinical research

I am working on the analysis of an observational clinical study. It is basically a control/intervention study, where the intervention group received a novel protocol. In order to assess its ...
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Propensity score matching R swap control and treatment

I am analyzing observational data and want to perform propensity score matching. I would like to compare males and females matched for age,smoking status, bmi, etc. When I coded the male gender as 0 ...
user18802819's user avatar
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How to construct a common control group in two cohorts with different disease outcomes?

Question details: I have cohort data with a total of 100,000 participants. For some reason, I need to select 2000 participants as controls to construct a control group. I have 3,000 cases of disease A ...
Mou Yuanlin's user avatar
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How to Evaluate Interaction Effects in Propensity Score-Matched Samples:

Suppose I want to study the association between an exposure X and outcome Y, and I have used the propensity score to match each exposed subject with those unexposed but with similar characteristics ...
zjppdozen's user avatar
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Matching numerical dataset to closest set in library

I have numerical, 1D datasets from measurements that I each want to match to the closest dataset in a library containing similar datasets. Coding is done in Matlab. Unfortunately I am not too ...
Jan's user avatar
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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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Propensity score matching - within subsample analysis

I am trying to understand the impact of a graduate degree in mathematics on the wages of students compared to other degrees. I have been running a probability score matching model using a logarithm ...
Giulio Cavallari's user avatar
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1 answer
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Do I have to use the exact same variables in each step, if I have a two-step propensity score match followed by a regression?

I am using the propensity score match to grow my sample based on a smaller dataset of existing units that received the treatment. The match will find more likely units from its large population that ...
LifelongLearner2's user avatar
2 votes
1 answer
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How to obtain the (weighted or unweighted) L1 imbalance measure for "raw" data

I have two questions regarding the JASA paper, Multivariate Matching Methods That Are Monotonic Imbalance Bounding, by Iacus et al. (2011), the authors produce Figure 2 (left panel) where they compare ...
yungmist's user avatar
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38 views

Is it advised to do treatment-covariate interactions to estimate the average treatment effect when using exact matching?

I am using method = “exact” from the MatchIt package. In the vignette of MatchIt, it is ...
klin's user avatar
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5 votes
1 answer
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Should I use independent- or propensity score matching?

I work on data form an observational study (n=34) and want to compare the outcome of the individuals to an historic cohort (n=600). Subjects in the observational study received additional treatment ...
Hans Dak's user avatar
1 vote
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
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56 views

Estimating treatment effect with G computation

I used the following code to obtain ATE. Before using the 'marginal effect' package, I examined the coefficients of the model and noticed that the 'symptom' variable has a high standard deviation. Is ...
Mahboobeh Taherizadeh'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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4 votes
1 answer
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Resample from a sample to match a desired distribution

Suppose I have observations $x_1,\dots,x_n$, sampled iid from some distribution on $\mathbb{R}$, with pdf $p(x)$. Suppose I wish I had a sample from the distribution with pdf $q(x)$. Is there a way ...
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1 vote
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Does propensity score matching reduce OVB?

Suppose you have data on a large population, a small proportion of which is treated. Let's assume there are enough treated data points that you don't need propensity scores for data reduction. There ...
Mohan's user avatar
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2 votes
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Using Difference-in-Differences With No Natural Groupings

I'm running across a number of analyses at my organization where people are doing difference-in-differences on individual-level data with no natural groupings. For example, one looked at the effect ...
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2 votes
1 answer
75 views

Matching order based on grouping variable

I want to match in a certain order. I am aware of the argument order, available in package {MatchIt} - but this refers to the ...
geek45's user avatar
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0 answers
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Is it possible to estimate effects using Bayesian modelling after matching?

I am following [Greifer 2023][1] to estimate the effect size after (genetic) matching, where I am using bootstrapping to estimate the confidence intervals. Since I have a hierarchical setup with ...
guest1927's user avatar
4 votes
1 answer
59 views

How to report the effect of covariates in post-matching analysis?

I have conducted genetic matching using the MatchIt package in R with the ultimate aim to compare the effect of the transition to a new form of land management. I ...
guest1927's user avatar
1 vote
1 answer
76 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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Matching approaches: hard match with propensity score (or logit) or hard match followed by propensity score matching

I have a population-based cohort and am exploring between two approaches to 1:1 matching: Hard matching that includes the logit of the propensity score (one step) Hard matching then creating a ...
Joll's user avatar
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0 votes
0 answers
104 views

MatchIt reusing control units multiple times in a panel data setting

I have a panel data with treated and non-treated firm year-quarters, and I would like to do propensity score matching to generate a treated/control firm subsample. The matching needs to be 1 to 1 and ...
Henry Wang's user avatar
3 votes
1 answer
67 views

Is running P(T=1|Y,X) a recommended way to help diagnose the unconfoundedness assumption in causal inference?

I've always heard that there isn't a lot of ways to diagnose the assumption that you've found the correct set of confounders in causal inference in confounder-control studies, and the best we can do ...
user11513145's user avatar
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28 views

Can propensity score matching be used in nested case-control study?

I'm poor with epidemiological methodology and I'm working on a study that look at the effects of a binary exposure on three time-to-event outcomes in a large cohort (general population). As the ...
Tylorc's user avatar
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1 vote
1 answer
710 views

Matching controls in staggered difference-in-differences

I am planning a staggered difference-in-differences analysis examining the impact of a change in health policy at the county level. The policy was implemented at different times in different counties, ...
telegraph's user avatar
2 votes
1 answer
32 views

Use of simple statistics for matched study designs

This might be a silly question, but I have always found this confusing. In a case-control study one selects participants to the study based on having or not having the outcome, in addition to some set ...
dean's user avatar
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1 vote
1 answer
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How to recover total distance in of optimal full matching in MatchIt?

It is easy to recover the total distance using optmatch::fullmatch, e.g., ...
SEL's user avatar
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0 answers
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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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1 answer
78 views

Do exact matching variables need to be included when estimating effects after matching with the MatchIt package?

Do exact matching variables need to be included when estimating effects after matching with the MatchIt package? Take the following example (adapted from the MatchIt...
nicholas's user avatar
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0 answers
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Converting PanelMatch Estimates to Marginal Effects in Non-Linear Outcome Models

I am interested in using the {PanelMatch} package for a project that I am working on where the conventional matching/weighting framework is expanded to account for the complexities of the panel data ...
Brian Lookabaugh's user avatar
3 votes
1 answer
36 views

Propensity scores with past observations

Theoretical question here: is it possible (and justifiable) to do propensity score matching when all control units come from a different time period? For example, imagine there is a programme for 18 ...
Rob_research's user avatar
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0 answers
21 views

Applied statistical propensity score matching with panel data. (Economics)

Im am working on a university project that tries to estimate the kausal impact of childbirth and and labour income. The problem can be described as follows. In theory you would need childbirth to be ...
Alexander Schönefeld's user avatar
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0 answers
55 views

What are the current guidelines for performing sensitivity analysis on matched data that is able to compare bias in reference to other covariates?

I am trying to perform sensitivity analysis on a causal inference (observational study) problem in a dataset that has a binary outcome and binary treatment. I've applied matching and g-computation ...
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