Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [counterfactuals]

An *if* statement in which the condition is untrue or unrealized. Used in causal analysis for comparing potential outcomes under different hypothesized conditions.

An if statement in which the condition is untrue or unrealized. Used in causal analysis for comparing potential outcomes under different hypothesized conditions.

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Strong ignorability: confusion on the relationship between outcomes and treatment

In the research area of potential outcomes and individual treatment effect (ITE) estimation, a common assumption called ''strong ignorability'' is often made. Given a graphical model with the following variables: treatment $T=\{0,1\}$ (e.g. giving…
SolingerMuc
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Textbook recommendations covering machine learning techniques for causal inference?

Over the past 15 years there has been progress in adapting machine learning methods for causal inference. For example: targeted learning, double machine learning, causal trees. Is there a textbook that covers the current range of techniques? I…
RobertF
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Instrumental variables: In which cases would the average treatment effect on the treated (ATT) and local average treatment effect (LATE) be similar?

It seems that if the proportion of always-takers in the control group (to whom eligibility was not assigned) is much smaller than the proportion of compliers in the treatment group (to whom eligibility was assigned), then ATT would be similar to…
Aqqqq
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Conterfactual estimation in machine learning model

There are various techniques to build counterfactual estimations of certain variables for linear models in observational studies. Some of those are based on comparing the change in the predicted outcome when varying the exposure variable of interest…
Bakaburg
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Counterfactual Estimation - Common Practices in Applied Causality

I am quite new to the topic and trying to figure out a workflow for causal analysis. My aim is to establish a baseline of ATE (I think) and then experiment with disentangled representations and machine learning (e.g CEVAE). I am mainly going to use…
Nikos H.
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Adjustment formula for counterfactuals: can we get rid of $X=x$?

Pearl et al. "Causal Inference in Statistics: A Primer" (2016) p. 108 contains the following adjustment formula (based on the backdoor criterion) for probabilities of counterfactuals expressed using observed data: \begin{align} P(Y_x|X=x')=\sum_z…
Richard Hardy
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How to understand probability of Necessity (PN) ≥ 100%, as in this example from 'Causal Inference in Statistics a primer'

In the book 'Causal Inference in Statistics A Primer' By Pearl et al. there is an example towards the end, (Ex 4.5.1 page 119) that calculates the probability of necessity PN = 1, and the authors state 'The data provides us with 100% assurance that…
Mint
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Counterfactuals in Econometric Modeling (Abortion-Crime Hypothesis Revisited)

Donohue and Levitt (2019) recently published a working paper revisiting the abortion-crime link. My question is specific to equation (2) in their paper (see below): $$ ln(CRIME_{st}) = \beta_{1}ABORT_{st} + X_{st}\Theta + \gamma_{s} + \lambda_{t} +…
Thomas Bilach
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The counterfactual model for causation

My professional training took place in the late 1990's and I don't recall hearing some of the terminology that seems nigh-universal nowadays. I believe the accepted name is "counterfactual model for causation", often used by Sander Greenland and…
Brent Hutto
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How can I update a disease prediction model with new treatment group data while maintaining the original causal relationships?

Context: I have a prediction model which predicts the probability of getting a disease. This prediction model has been created based on data of patients who did not get any form of treatment. I use this model on new patients. Patients which have a…
Koot6133
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Should inverse probability weighting be used in two-way fixed-effects panel regression?

Let's assume a (balanced) panel data set with two measurement points $t_0$ and $t_1$, where $t_0$ may be considered as the baseline. Some of the ID's are treated at $t_1$, i.e. $D=1$, the assignment is non-random and uneven, though. The data include…
jay.sf
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Using counterfactual modeling techniques to assess racial bias in predictive models

My team at a health insurance company is discussing how we might measure racial bias in the various predictive models our company uses to assess future health risk (such as annual medical cost or probability of major surgery). This has jumped to the…
RobertF
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Linear model: potential outcome framework vs. structural causal model

From my reading about the potential outcomes framework (POF) and structural causal models (SCM), I understand that both perspectives have been shown to be equivalent but take different starting points. In particular, the POF takes as a starting…
curious
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find upper and lower bounds on average causal effect $\theta$

I am working on some problems in All of statistics by Wasserman, and I am not quite sure how to tackle this problem. Suppose you are given data $(X_1, Y_1), \dots, (X_n, Y_n)$ from an observational study where $X_i \in \{0, 1\}$ and $Y_i \in \{0,…
MoneyBall
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Difference between the counterfactual mean and average treatment effect

I am new on the causality topic, I don't know the difference between the average treatment effect and counterfactual mean. Can anyone tell me?
cookie monster
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