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Questions tagged [causality]

The relationship between cause and effect.

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Estimating Individual-Level Causal Effects Under Selection on Observables

The value of a randomized controlled trial is that the random assignment of treatment ensures that no confounding is biasing the relationship between treatment and outcome and, therefore, a simple ...
Brian Lookabaugh's user avatar
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On assumptions of local projection method

It is well known that Jorda(2005) proposed the following model called local projection: $$y_{t+h} - y_{t-1} = \beta_h shock_{t} + \gamma_h ctr_{t-1} + \epsilon_{t,h}, h = 0,1,2,\dots,H.$$ I am trying ...
zyy's user avatar
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How would I do sensitivity analysis for other models? (logistic regression, random forest, Cox proportional hazard) [closed]

I wanted to know how to conduct sensitivity analysis for causal inference on my models. Right now I've used logistic regression, a random forest model, and in another study I have used a Cox ...
statsfan345's user avatar
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Understanding Ignorability and Confounding Variables

I am reading Data Analysis Using Regression and Hierarchical Models and am confused by the concept of ignorability. The description in the book seems to say different things. Said another way, we ...
RSHAP's user avatar
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Sandwich variance estimator or bootstrap-based variance for stabilized inverse probability weighting (IPW)

Multiple published papers describe IPW as akin to having population with multiply copies of the same individuals. Hence, the correlation should be accounted and corrected using sandwich variance ...
tatami's user avatar
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How to deal with data edge cases when applying Pearl intervention formula for estimation?

Given the causal graph $(Z\to X$, $Z\to Y$, $X\to Y)$, according to Pearl’s intervention, the effect of intervening $X$ on $Y$ can be estimated as $$P(Y=y|\operatorname{do}(X=x)) = \sum_z P(Y=y|X=x,Z=...
Diep Luong's user avatar
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Moderated Mediation in a "Two-Step" Structural Equation Model

Can someone please help me figure out how to include a moderator in the structural equation model (SEM) below? It's a "two-step" model in the sense that there are two causal linkages, one ...
JalapenoSupremacy's user avatar
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Causal mediation analysis via g-computation - why randomly permute the simulated mediator?

I have been reading about causal mediation analysis in settings where there is time-varying confounding of the mediator-outcome relationship. The estimands in question are the randomized analogues of ...
MartinQLD's user avatar
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When the direction of causation can plausiblely run in either direction, is there a good stat approach to distinguishing relative strength?

I am interested in an instance where it is plausible that ideology influences economic outcome, and also that economic outcomes influence ideology. Assuming that I have good and relavent measures of ...
andrewH's user avatar
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Rather Than Framing Causal Inference as "How Much X Causes Y to Change", Can You Frame Causal Inference As "X Explains _% of the Variation in Y"

Most causal research designs seek to estimate a causal effect and interpret that causal effect as a marginal effect (a 1-unit shift in X leads to a _ amount of change in Y). However, as I've spent ...
Brian Lookabaugh's user avatar
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Meta-learner trained on matched data [closed]

I am trying to estimate the average treatment on the treated. I have used propensity score matching first, to create the control and treatment groups. I end up having quite small group sizes (1500 ...
gummy's user avatar
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Grid-level spatial fixed effects (with time and seasonality)

I have panel data with reported human-wildlife conflicts with a date and grid cell location: I'm trying to isolate the impact that extreme precipitation and temperature have on conflict rates. I am ...
spes's user avatar
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Can I interpret the size of my regression coefficient when I only control for confounders and not non-confounding covariates?

I am very confused at the moment, for my bachelor's thesis I am performing a Panel Individual Fixed Effects analysis and have only controlled for confounders. I was under the assumption that when I ...
user418117's user avatar
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What is the math rationale behind the inverse probability weighting?

Papers say IPTW (inverse probability weighting) is superior to PSM (propensity score matching) because it does not necessarily drop observations, whereas PSM drops those observation not paired. IPTW ...
Tom Hsiung's user avatar
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Experiment design to determine effect of ads on etsy shop performance

I am looking to design an experiment to determine the impact of turning on etsy ads for my shop. I opened a shop for plant pots and have made 27 sales in the 4 months I have been open. I am ...
Nick G's user avatar
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Causal variables and causal independence of the noise variables

In causality, my understanding is that, if we have causal variables $S_i$, we must have / assume that the (exogenous?) noise variables associated with each $S_i$ are causally independent of each other....
The Pointer's user avatar
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How can restricted randomization to achieve covariate balance lead to imbalance in unobserved variables?

In literature, designing an experiment is considered a trade-off between covariate balance and robustness. For example Harshaw et al. (2024) writes In an effort to make the estimators more precise, ...
retodomax's user avatar
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Estimating effects in the presence of a mediator

Suppose that one is interested to compare the effect of biological age versus "cognitive age" on a variety of outcomes. Cognitive age is measured by testing intelligence. The outcomes are a ...
Sam's user avatar
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Is the total effect from HIV on stroke equal to the direct effect in the Table 2 fallacy paper by Westreich and Greenland

In the paper The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients by Daniel Westreich and Sander Greenland, the authors present a simple example to illustrate how to ...
Boussens-Dumon Grégoire's user avatar
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How do you interpret estimates when the model is the same for two exposure variables?

I want to investigate the direct effect of two environmental variables $X_1$ and $X_2$ on a quantity $Y$. $X_1$ and $X_2$ are connected together and with two other environmental variables $X_3$ and $...
Boussens-Dumon Grégoire's user avatar
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Can the exclusion restriction in instrumental variable estimation be expressed as $\mathrm{cov}(Z, Y | X) = 0$?

In instrumental variable (IV) estimation of the regression model $Y = \alpha + \beta X + \epsilon$, where $Y$ is the outcome, $X$ is an endogenous independent variable, $\epsilon$ is the error term, ...
Peter Jordanson's user avatar
4 votes
1 answer
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What are indirect and common cause links?

This question is about the PCMCI method (as described by Runge et al 2019) for finding causal relationships in time series data. The 2nd part of the process, called Momentary Conditional Independence (...
quant's user avatar
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Why does dimensionality affect significance and effect size in a Full Conditional Independence (FullCI) test?

The 2018 Runge et al paper titled "Detecting causal associations in large non linear time series datasets" describes the PCMCI method. It compares the new PCMCI method with another method ...
quant's user avatar
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Testing Causal Relationships with Interference as the Primary Motivation

One of the core components of causal inference is the consistency of treatment. One element of this is the absence of interference, where the exposure in a spatial/temporal unit does not affect the ...
Brian Lookabaugh's user avatar
3 votes
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Merging an administrative dataset with survey data in the context of a regression discontinuity design: what things should I consider?

I have an administrative dataset that contains the running variable for a Regression Discontinuity Design (RDD) study. I plan to merge this dataset with survey data that was collected for other ...
Santiago Valdivieso's user avatar
4 votes
1 answer
64 views

causal effect and exogeneity in small samples vs in large samples

Under this title two questions are included. In regression of Y on X and controls Z with random error e, In small sample if strictly exogeneity, i.e. E(e|X,Z)=0, is satisfied, does it means the ...
jerry's user avatar
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Staggered diff-in-diff vs Stacked diff-in-diff

Just trying to better understand what really is the technical difference between staggered diff-in-diff and stacked diff-in-diff. I understand that TWFE staggered DiD has its own troubles and that ...
Econ Wanderer's user avatar
2 votes
1 answer
32 views

Propensity score weighting with post-treatment variables

It's been emphasized to balance pre-treatment variables when doing propensity score weighting (PSW) as balancing post-treatment variables can introduce bias. I want to ask your insights on the case ...
HYL's user avatar
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Interpretation of estimates for adjusted variables

I've started using DAG to improve the construction of my regression models and I was wondering if it made any sense to interpret the estimates I get for variable I adjusted for in my model ? Let's say ...
Boussens-Dumon Grégoire's user avatar
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What causal inference method is suitable to identify the effect of different components of a webpage on the decision to purchase?

I am getting started with causal inference for a project where I want to evaluate the causal effect of different components of a webpage on users' decision to purchase. For illustration purposes, let'...
gummy's user avatar
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7 votes
3 answers
153 views

Does an instrumental variable require independence between the instrument and treatment?

An instrumental variable Z is a legitimate instrument for T (the treatment) if the following hold: Z has a causal effect on Y that is fully mediated by T (i.e. no direct effect from Z to Y, and the ...
aranglol's user avatar
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How to correctly specify "exposure" and "outcome" in dagitty/ggdag, when modelling the inverse relationship (exposure ~ outcome)?

I am trying to use the package ggdag in R to better understand the results of my modelling. If I wish to model the relationship ...
Ildifa's user avatar
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1 answer
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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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What is the best way to model the impact of multiple therapies/diagnostic tests on healthcare resource utilization and costs pre- and post-treatment?

I have a methods question that I'm hoping this group might be able to answer. I'm planning an causal inference analysis in R where we want to see how the use of Diagnostic Test 1 rather than ...
cemarg's user avatar
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Techniques for uplift modelling/Conditional Average Treatment Effect(CATE) estimation for observational data

I have very recently started learning CI and was going through this very famous paper:https://proceedings.mlr.press/v67/gutierrez17a.html which mentions that Randomised Control Trials are an essential ...
Abhay Gupta's user avatar
5 votes
2 answers
182 views

How to determine if a directed edge is visible? What does visibility tell us?

Both of the above PAGs (Partial Ancestral Graph) are generated using the FCI (Fast Causal Inference) algorithm under identical conditions, the only difference being that the one on the right is ...
ColorStatistics's user avatar
5 votes
1 answer
115 views

Why are these 2 MAGs Markov Equivalent? DAGs, MAGs, and PAGs

On page 1443 of the linked paper, the authors present the following causal DAG (Directed Acyclic Graph) with a latent variable (Profession). On the following page, they present the 2 MAGs below with ...
ColorStatistics'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 ...
user321627's user avatar
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2 votes
1 answer
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For estimating weights in the Synthetic Control Method, what linear combination of the outcomes and symmetric matrix are used?

I am reading Synthetic Control Methods for Comparative Case Studies Paper, and on pg. 5 it states how to estimate Synthetic Control Weights. The following is a summary of the methods in the paper. I ...
user321627's user avatar
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1 vote
1 answer
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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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7 votes
3 answers
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Is multicollinearity a "warning sign" for causal inference?

Suppose we are inferring whether $A$ causes $B$, while holding $N = [N_0, N_1, \ldots, N_n]$ constant and we find $N_i$ correlates well but not perfectly with $A$. There are four reasons to exclude $...
charmoniumQ's user avatar
6 votes
1 answer
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Rule 2 in Pearl's do calculus

Rule 2 in Pearl's do calculus states that if $Y\perp_{G_{\bar T, \underline Z}}Z|T, W$, then $P(y|do(t), do(z), w) = P(y|do(t), z, w)$. Is the following DAG $G$ a counterexample? How to understand ...
Lulu's user avatar
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3 votes
1 answer
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Understanding how to evaluate the integral causal-effect expression

I have this expression $$ p( Y \mid \text{do}(Z=z)) = \int_{B, S, W, X} dBdSdWdX \ \ P(B | S) P(W | B, S) P(X | B, S, Z=z) \left[ \int_{Z'} dZ' P(Z'| B,S,W) P(Y | B, S, W, X, Z') P(S) \right] $$ ...
Astrid's user avatar
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4 votes
1 answer
114 views

Inferences on ratio of branch means in randomized experiment

It's generally well known that the difference of means is an unbiased estimator of the Average Treatment Effect in randomized experiments: $\mathbb{E}[Y|A=1]-\mathbb{E}[Y|A=0]$ is unbiased for $\...
user1993951's user avatar
1 vote
0 answers
40 views

Can Multiple Regression Output (coefficients, ratios, etc.) Be Given a Predictive Interpretation?

Generally speaking, regression output can be given a causal interpretation for typically one variable in the model (that is under the assumption of no unobserved confounding and this is not to speak ...
Brian Lookabaugh's user avatar
6 votes
2 answers
134 views

Is it possible to evaluate causal algorithms on real world observational data?

Lot of times I get asked to use causal algorithms (e.g. algorithms estimating intervention results, or in general causal inference algorithms) and to compare them against non-causal prediction ...
DaSim's user avatar
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2 votes
1 answer
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Granular difference-in-differences with non-repeating unit of observation

I want to analyze changes in characteristics of job postings around an (exogenous) event. However, rather than conducting the analysis at the job poster level (e.g., a company or geographic area), my ...
kurofune's user avatar
3 votes
1 answer
22 views

How to interpret the conditional expectation mean $E[Y(0)\mid Z=1]$ where $Y(0)$ is the potential outcome on control and $Z$ the treatment assignment?

How can I interpret the conditional expectation mean $E[Y(0)\mid Z=1]$ where $Y(0)$ is the potential outcome on control and $Z$ the treatment assignment? I believe it is the "expected outcome if ...
user321627's user avatar
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2 votes
1 answer
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Why the confusion in Difference-in-Differences with pre-intervention/post-intervention periods and treated/control units?

In Difference-in-Differences, it typically has a pre-intervention and a post-intervention periods. This is coupled with treated and control units. From what I understand, since the comparison for the ...
user321627's user avatar
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0 votes
1 answer
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Is the treatment effect in the Synthetic Controls Method the ATE or the ATT?

In the Synthetic Controls Method, the treatment effect for a time $t$ is given to be the difference in outcomes for the treated unit and control unit, in the post-treatment period. In this regard, I ...
user321627's user avatar
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